METHODOLOGY ARTICLE Open Access
Decon olu ion o bulk blood eQTL e ec s
in o immune cell subpopula ions
Raúl Agui e-Gamboa
1†
, Niek de Klein
2†
, Jenni e di Tommaso
1†
, Annique Cla ingbould
2
,
Monique GP an de Wijs
2
, Dylan de V ies
2
, Ha m B ugge
2
, Roy Oelen
2
, U mo Võsa
1,3
, Ma ia M. Zo o
1
,
Xiaojin Chu
1,4
, Oli ie B. Bakke
1
, Zuzanna Bo ek
1
, Isis Ricaño-Ponce
1
, Pa ick Deelen
2,5
, Cheng-Jiang Xu
4,7
,
Mo is Swe z
1,5
, I is Jonke s
1
, Sebo Wi ho
1
, I ma Joos en
6
, Se ena Sanna
1
, Vinod Kuma
1,7
, Hans J. P. M. Koenen
6
,
Leo A. B. Joos en
7
, Mihai G. Ne ea
7,8
, Cisca Wijmenga
1
, BIOS Conso ium, Lude F anke
1†
and Yang Li
1,4,7*†
* Co espondence: Yang.Li@
helmhol z-hzi.de
†
Raul Agui e-Gamboa, Niek de
Klein Jenni e di Tommaso a e
con ibu ed equally o his wo k.
†
Lude F anke and Yang Li a e join ly
di ec ed his wo k
1
Depa men o Gene ics, Uni e si y
o G oningen, Uni e si y Medical
Cen e G oningen, G oningen, he
Ne he lands
4
Cen e o Indi idualised In ec ion
Medicine (CiiM) & TWINCORE, join
en u es be ween he
Helmhol z-Cen e o In ec ion
Resea ch (HZI) and he Hanno e
Medical School (MHH),
Feodo -Lynen-S . 7, 30625
Hanno e , Ge many
Full lis o au ho in o ma ion is
a ailable a he end o he a icle
Abs ac
Backg ound: Exp ession quan i a i e ai loci (eQTL) s udies a e used o in e p e
he unc ion o disease-associa ed gene ic isk ac o s. To da e, mos eQTL analyses
ha e been conduc ed in bulk issues, such as whole blood and issue biopsies, which
a e likely o mask he cell ype-con ex o he eQTL egula o y e ec s. Al hough his
con ex can be in es iga ed by gene a ing ansc ip ional p o iles om pu i ied cell
subpopula ions, cu en me hods o do his a e labo -in ensi e and expensi e. We
in oduce a new me hod, Decon2, as a amewo k o es ima ing cell p opo ions
using exp ession p o iles om bulk blood samples (Decon-cell) ollowed by
decon olu ion o cell ype eQTLs (Decon-eQTL).
Resul s: The es ima ed cell p opo ions om Decon-cell ag ee wi h expe imen al
measu emen s ac oss coho s (R ≥0.77). Using Decon-cell, we could p edic he
p opo ions o 34 ci cula ing cell ypes o 3194 samples om a popula ion-based
coho . Nex , we iden i ied 16,362 whole-blood eQTLs and decon olu ed cell ype
in e ac ion (CTi) eQTLs using he p edic ed cell p opo ions om Decon-cell. CTi
eQTLs show excellen allelic di ec ional conco dance wi h eQTL (≥96–100%) and
ch oma in ma k QTL (≥87–92%) s udies ha used ei he pu i ied cell subpopula ions
o single-cell RNA-seq, ou pe o ming he con en ional in e ac ion e ec .
Conclusions: Decon2 p o ides a me hod o de ec cell ype in e ac ion e ec s om
bulk blood eQTLs ha is use ul o pinpoin ing he mos ele an cell ype o a
gi en complex disease. Decon2 is a ailable as an R package and Ja a applica ion
(h ps://gi hub.com/molgenis/sys emsgene ics/ ee/mas e /Decon2) and as a web
ool (www.molgenis.o g/decon olu ion).
Keywo ds: eQTL, Decon olu ion, Cell ypes, Immune cells
© The Au ho (s). 2020 Open Access This a icle is licensed unde a C ea i e Commons A ibu ion 4.0 In e na ional License, which
pe mi s use, sha ing, adap a ion, dis ibu ion and ep oduc ion in any medium o o ma , as long as you gi e app op ia e c edi o
he o iginal au ho (s) and he sou ce, p o ide a link o he C ea i e Commons licence, and indica e i changes we e made. The
images o o he hi d pa y ma e ial in his a icle a e included in he a icle's C ea i e Commons licence, unless indica ed o he wise
in a c edi line o he ma e ial. I ma e ial is no included in he a icle's C ea i e Commons licence and you in ended use is no
pe mi ed by s a u o y egula ion o exceeds he pe mi ed use, you will need o ob ain pe mission di ec ly om he copy igh
holde . To iew a copy o his licence, isi h p://c ea i ecommons.o g/licenses/by/4.0/. The C ea i e Commons Public Domain
Dedica ion wai e (h p://c ea i ecommons.o g/publicdomain/ze o/1.0/) applies o he da a made a ailable in his a icle, unless
o he wise s a ed in a c edi line o he da a.
Agui e-Gamboa e al. BMC Bioin o ma ics (2020) 21:243
h ps://doi.o g/10.1186/s12859-020-03576-5
Backg ound
Fo many o he gene ic isk ac o s ha ha e been associa ed o immune diseases by
genome-wide associa ion s udies (GWAS), he molecula mechanism leading o disease
emains unknown [1]. Mos o hese gene ic isk a ian s a e loca ed in he non-coding
egions o he genome, implying ha hey play a ole in gene egula ion [2,3]. Exp es-
sion quan i a i e ai locus (eQTL) analysis p o ides a way o cha ac e ize he egula-
o y e ec o hese isk ac o s in humans, and many eQTL s udies ha e now been
ca ied ou using bulk issues, o example, whole blood [4,5]. Howe e , bulk issues
comp ise many di e en cell ypes, and gene egula ion is known o a y ac oss cell
ypes [6–8]. In ecen yea s, e o s o desc ibe eQTL e ec s in pu i ied cell subpopula-
ions ha e been ca ied ou in speci ic cell ypes [9]. Un o una ely, he leng h and cos
o he s udy p o ocols ha e limi ed hese s udies o small sample sizes and only a ew
cell ypes. Cu en de elopmen s on single cell (sc) RNASeq echnologies ha e gi en
ise o sc-eQTLs, an app oach ha , al hough p omising, is s ill bound o a limi ed
numbe o indi iduals, which he eby limi s he numbe o de ec able cell ype in e -
ac ion (CTi) eQTLs. Ne e heless, he abili y o pinpoin he CT in which a isk ac o
exe s an eQTL e ec could help us o unde s and i s ole in disease.
S a is ical app oaches o de ec CT e ec s using issue exp ession p o iles ha e
mainly been de eloped o e alua e gene by en i onmen in e ac ion (GxE) e ms, o
example o de ec CT eQTLs o myeloid and lymphoid lineages using only whole
blood gene exp ession and by e alua ing he in e ac ion be ween geno ype and cell p o-
po ions o neu ophils and lymphocy es in whole blood [10]. A second s udy linked
eQTL genes o p oxy genes h ough co ela ion; hese p oxy genes we e hen associ-
a ed wi h in insic o ex insic ac o s such as cell p opo ions o in lamma ion ma ke s
[11]. Howe e , hese e o s ocused on exploi ing only one GxE e m, o on indi ec ly
linking he CT p opo ions o gi en eQTL, a he han di ec ly asce aining he in e -
ac ion be ween all he main cell p opo ions comp ising he bulk issue and geno ype.
Un o una ely, quan i ying cell p opo ions, in pa icula a e subpopula ions ( o al
abundance ≤3% in ci cula ing whi e blood cells), is expensi e and ime-consuming.
Hence, quan i ying immune cell p opo ions in la ge unc ional genomics coho s is
no common p ac ice.
He e we p esen and alida e Decon2, a compu a ional and s a is ical amewo k ha
can (1) p edic he p opo ions o known ci cula ing immune cell subpopula ions
(Decon-cell), and (2) combine hese p edic ed p opo ions wi h whole blood gene ex-
p ession and geno ype in o ma ion o assign bulk eQTL e ec s in o CTi eQTLs
(Decon-eQTL). Ou wo-s ep amewo k p o ides an imp o emen o e p e iously
published me hods. Unlike ea lie me hods [12], Decon-cell does no ely on any p io
in o ma ion abou ansc ip ome p o iles om pu i ied cell subpopula ions. I only e-
qui es quan i ica ion o he cell p opo ions comp ising he bulk issue, in his case
whole blood. Decon-cell iden i ies signa u e genes ha co ela e wi h cell p opo ions
in a bulk issue. Secondly, Decon-eQTL is he i s app oach in which all majo cell
p opo ions ( he majo cell ypes o which he sum o p opo ions pe sample is ap-
p oxima ely 100%) o bulk blood issue a e inco po a ed in o an eQTL model simul an-
eously. Decon-eQTL can hen be used o sys ema ically es o any signi ican
in e ac ion be ween each CT and geno ype, while also con olling o he e ec on ex-
p ession o he o he cell ypes.
Agui e-Gamboa e al. BMC Bioin o ma ics (2020) 21:243 Page 2 o 23
We gene a ed he Decon-cell p edic i e models using da a om he 500FG coho
[13], whe e quan i ica ion o immune cell ypes was ca ied ou using FACS [14] and
RNA-Seq-based bulk whole blood ansc ip ome p o iles we e a ailable o 89 samples
[15]. By using a c oss- alida ion app oach, we we e able o accu a ely p edic 34 ou o
73 cell sub ypes using only whole blood gene exp ession. Fo alida ion, we applied
Decon-cell o h ee independen coho s (Li elines Deep [16], n= 627; Leiden Longe i y
coho [17], n= 660 and he Ro e dam S udy [18], n= 773) o which bo h blood
RNA-seq and measu ed cell p opo ion da a a e a ailable (neu ophils, lymphocy es
and CD14+ monocy es and g anulocy es). Addi ionally, we benchma ked Decon-cell
p edic ion pe o mance agains wo o he exis ing me hods ha quan i y immune cell
composi ion using gene exp ession p o iles om whole blood on hese h ee independ-
en coho s. A e showing ha we can accu a ely p edic ci cula ing immune cell p o-
po ions, we applied Decon-cell o es ima e cell p opo ions in 3194 indi iduals om
he BIOS coho [16,19–21] o whom bo h whole blood RNA-seq and geno ypes we e
a ailable. The BIOS coho is a aluable esou ce o unc ional genomics s udies whe e
ex ensi e cha ac e iza ion o he gene ic componen on gene exp ession [11] and epi-
gene ics [22] ha e been pe o med. We in eg a ed whole blood exp ession and geno-
ype in o ma ion and p edic ed cell p opo ion wi h Decon-eQTL o decon olu e 16,
362 signi ican whole blood cis-eQTLs op e ec s in o CT in e ac ing eQTLs (CTi
eQTLs). These decon olu ed CTi eQTL esul s we e comp ehensi ely alida ed using
ansc ip ome p o iles om pu i ied cell subpopula ions [23], eQTLs and ch oma in
ma k QTLs om pu i ied cell ypes [9] and eQTLs om single-cell expe imen s [24].
We also sys ema ically compa ed he pe o mance o Decon-eQTL agains he mos
used me hod [10] ha de ec cell ype eQTL e ec s using whole blood exp ession
p o iles.
Resul s
Decon-cell accu a ely p edic s he p opo ions o known immune cell ypes
In o de o assign he cell ypes om which an o e all eQTL e ec om a bulk issue
sample (e.g. whole blood) a ise, we need h ee ypes o in o ma ion: geno ype da a, is-
sue exp ession da a and cell ype p opo ions (Fig. 1). He e we p opose a compu a-
ional me hod ha p edic s he cell p opo ions o known immune cell ypes using
gene signa u es in whole blood exp ession da a using a machine-lea ning app oach.
Decon-cell employs he egula ized eg ession me hod elas ic ne [26] o de ine se s o
signa u e genes o each cell ype. In o he wo ds, hese signa u es we e selec ed as
ha ing he bes p edic ion powe o indi idual cell p opo ions.
The e a e 89 samples in he 500FG coho wi h bo h whole blood RNA-seq and
quan i ica ion o 73 immune cell subpopula ions by FACS. This da a was used o build
he p edic ion models o es ima ing cell subpopula ions by Decon-cell. Fi s , we de e -
mined which o he 73 cell subpopula ions could be eliably p edic ed by Decon-cell. A
wi hin-coho c oss- alida ion s a egy was employed by andomly di iding 89 samples
(Fig. 1) in o aining and es se s (70 and 30% o he samples, espec i ely). A e gen-
e a ing a model using each aining se , we applied he p edic ion models o each cell
ype o he samples in he es se s. We compa ed he p edic ed and measu ed cell p o-
po ion o each cell ype using Spea man co ela ion coe icien s o e alua e he
Agui e-Gamboa e al. BMC Bioin o ma ics (2020) 21:243 Page 3 o 23
p edic ion pe o mance. We epea ed his p ocess 100 imes and hen used he mean
o he co ela ion coe icien in all 100 i e a ions o e alua e he p edic ion pe o m-
ance. We we e able o p edic 34 ou o 73 cell subpopula ions using whole blood gene
exp ession da a a a h eshold o mean R ≥0.5 ac oss all 100 i e a ions (Fig. 2a, Supple-
men a y Fig. 1, Supplemen a y Table 1). The numbe o signa u e genes selec ed in ou
models o p edic ing cell p opo ions a ied ac oss he cell ypes, anging om 2 o
217 signa u e genes (Supplemen a y Fig. 2A, Supplemen a y Table 1), and hey we e in-
dependen o he a e age abundance o hese cell ypes in whole blood (R = 0.02, Spea -
man co ela ion coe icien , Supplemen a y Fig. 2A). In pa icula , cell ypes ha a e
abundan in whole blood (g anulocy es-neu ophils, CD4+ T-cells and CD14+ mono-
cy es) we e p edic ed wi h high con idence (co ela ion be ween p edic ed and mea-
su ed alues, R ≥0.73). Rema kably, we we e also able o p edic a numbe o less
Fig. 1 Wo k low o applica ion o Decon2 o p edic cell coun s ollowed by decon olu ion o whole blood
eQTLs. Using whole blood exp ession and FACS da a o 500FG samples, Decon-cell p edic s cell p opo ions
wi h selec ed ma ke genes o ci cula ing immune cell subpopula ions. Valida ions o Decon-cell we e
ca ied ou on h ee independen coho s o which measu emen s o neu ophils/g anulocy es,
lymphocy es and monocy es CD14+ we e a ailable along wi h exp ession p o iles o whole blood.
Benchma king o Decon-cell was pe o med agains CIBERSORT [25] and xCell [12]. Decon-cell was applied
o an independen coho (BIOS) o p edic cell coun s using whole blood RNA-seq. Decon-eQTL
subsequen ly in eg a es geno ype and issue exp ession da a oge he wi h p edic ed cell p opo ions o
samples in BIOS o de ec cell ype eQTLs. We alida ed Decon-eQTL using mul iple independen sou ces,
including exp ession p o iles o pu i ied cell subpopula ions, eQTLs and ch oma in ma k QTLs (cmQTLs)
om pu i ied neu ophils, monocy es CD14+ and CD4+ T cells [9], and single-cell eQTL esul s [24].
Benchma king o Decon-eQTL was ca ied ou o compa ison wi h a p e iously epo ed me hods ha
de ec ed cell ype–eQTL e ec s using whole blood exp ession da a, i.e. he Wes a e al. [10]
Agui e-Gamboa e al. BMC Bioin o ma ics (2020) 21:243 Page 4 o 23
abundan cell subpopula ions, including NK cells, CD8+ T-cells, non-NK T-cells (CD3-
CD56-), CD4+ cen al memo y, CD4+ e ec o memo y T-cells and egula o y T-cells
(Supplemen a y Fig. 2A), as de e mined by FACS. Cell ypes wi h a low p edic ion pe -
o mance (R < 0.5) a e hose ha ha e ew signa u e genes wi h exp ession le els ha
co ela e su icien ly (i.e. absolu e R < 0.3) wi h he measu ed cell p opo ions in whole
blood (Supplemen a y Fig. 2B-C). Fo each o he 34 p edic able cell ypes, we used
Decon-cell o build models o p edic ing hei cell coun s using all 89 samples om
he 500FG coho . These models we e applied o 3194 samples in an independen co-
ho (BIOS coho ) o p edic cell p opo ions o ci cula ing immune cell ypes o he
subsequen decon olu ion o eQTL e ec s.
In addi ion o wi hin-coho alida ion, we es ed ou cell p opo ion models using
h ee independen coho s (LLDeep, n= 627; LLS, n= 660; RS, n= 773) in which cell
ype abundances we e quan i ied using a Coul e coun e o neu ophils (g anulocy es
o RS), lymphocy es and CD14+ monocy es (Fig. 2b, Supplemen a y Fig. 3A-B). In
LLDeep, we we e able o accu a ely p edic hese h ee cell ypes wi h Spea man co el-
a ion coe icien s o R = 0.73, R = 0.89 and R = 0.73, espec i ely. Fo LLS and RS, he
p edic ion pe o mance was simila ly accu a e o neu ophils and lymphocy es (R =
0.76 o neu ophils, R = 0.84 o lymphocy es), bu less so o monocy es (R = 0.50 o
CD14+ monocy es and p opo ions in LLS and R = 0.74 o g anulocy es, R = 0.83 o
lymphocy es and R = 0.28 o CD14+ monocy es in RS).
Fig. 2 P edic ion o cell p opo ions using whole blood ansc ip ome by Decon-cell. aDis ibu ion o
p edic ion pe o mance (Spea man co ela ion coe icien ) o he 34 p edic able cell ypes in 100 i e a ions
o p edic ion wi hin he 500FG coho . bC oss- coho alida ion in an independen Li elines-Deep coho
(n= 627): he measu ed and p edic ed cell p opo ions o neu ophils (gi en by g anulocy es in 500FG),
lymphocy es and monocy es a e compa ed
Agui e-Gamboa e al. BMC Bioin o ma ics (2020) 21:243 Page 5 o 23

Nex , in o de o benchma k Decon-cell, we compa ed i s p edic ion pe o mance
agains wo o he exis ing ools ha quan i y he abundance o known immune cell
ypes using bulk whole blood exp ession p o iles: CIBERSORT [25] and xCell [12]. We
ob ained he p edic ed p opo ions by CIBERSORT and en ichmen sco es o ci cula -
ing immune cells by xCell o he samples in h ee di e en coho s: LLDeep, LLS and
RS (Supplemen a y Fig. 4A-B). Fo each cell ype, Decon-cell ou pe o ms CIBERSORT
and xCell (Supplemen a y Fig. 3B). The sca e plo s o p edic ed s measu ed alues
(Supplemen a y Fig. 3A, Supplemen a y Fig. 4A-B) u he demons a e ha he be e
pe o mance o Decon-cell is no due o cell p opo ion ou lie s.
Finally, we e alua ed whe he he signa u e genes showed CT exp ession in hei ele-
an pu i ied cell ypes using BLUEPRINT [23] RNA-seq da a om he pu i ied cell
subpopula ions. He e we ocused on cell ypes wi h mo e han h ee samples measu ed,
which included neu ophils, CD14+ monocy es, CD4+ T-cells and B-cells. The signa-
u e genes showed o e all highe exp ession in hei ele an cell subpopula ions com-
pa ed o o he cell subpopula ions. In e es ingly, he signa u e genes we e also able o
clus e he samples o he ele an CT using unsupe ised hie a chical clus e ing (Sup-
plemen a y Fig. 5A-D). Toge he , ou esul s demons a e ha he gene signa u es
iden i ied by Decon-cell using only whole blood gene exp ession da a a e p edic i e o
he p opo ions o ci cula ing immune cell subpopula ions.
To acili a e he cell p opo ion p edic ion o new samples using whole blood RNA-
seq, we ha e made he Decon-cell p edic ion models and gene signa u es a ailable in
an R package (Decon-cell) and as a web ool (www.molgenis.o g/decon olu ion). These
wo implemen a ions allow use s o p e-p ocess hei RNA-seq exp ession coun s and
es ima e cell p opo ions using he p e-es ablished models o 34 cell ypes in whole
blood. In addi ion, he Decon-cell R package allows use s o gene a e Decon-cell-like
gene signa u es o p edic hei own cell p opo ions, which equi es he inpu o bulk
exp ession p o iles and cell p opo ions o gene a e new Decon-cell p edic i e models.
Decon-eQTL iden i ies which cell ypes con ibu e o he whole blood eQTL e ec
As we know, eQTL analysis using whole blood bulk exp ession da a ails o dis inguish
be ween a gene al eQTL p esen in all cell ypes and an e ec mainly ound in a subse
o he cell ypes. We he e o e p opose a new app oach, called Decon-eQTL, ha as-
signs he o e all bulk eQTL in o CT e ec s. Using he cell p opo ions in whole blood,
i is possible o o mally es i he gene ic e ec is in e ac ing wi h he cell p opo ions.
Mo e explici ly, we include bo h he geno ype and all majo CT p opo ions o in e es
in a linea model, and sys ema ically es i he e is a signi ican in e ac ion e ec be-
ween geno ype and each o he cell p opo ions in he a ia ion o gene exp ession in
whole blood. A he same ime, he model used by Decon-eQTL con ols o he e ec s
o he emaining cell ypes on gene exp ession. In his way, whole blood exp ession
da a, geno ypes and (p edic ed) cell p opo ions can be in eg a ed o assign a CTi e ec
om a bulk eQTL (Fig. 1).
We applied Decon-eQTL o 3198 samples (BIOS coho ) wi h ansc ip ome le els
(RNA-seq), geno ype in o ma ion and cell p opo ions p edic ed by Decon-cell. Whole
blood cis-eQTL mapping yielded 16,362 whole blood eQTLs ( alse disco e y a e
(FDR) ≤0.05). Fo each o hese whole blood cis-eQTLs, we applied Decon-eQTL wi h
Agui e-Gamboa e al. BMC Bioin o ma ics (2020) 21:243 Page 6 o 23
a ocus on 6 majo cell subpopula ions: g anulocy es, CD14+ monocy es, CD4+ T-cells,
CD8+ T-cells, B-cells and NK cells. These cell ypes we e selec ed because he sum o
hei ela i e pe cen ages was close o 100% and none o hese cell ype pai s had an
absolu e co ela ion coe icien R ≥0.75. Decon-eQTL compu a ionally assigned 4139
CTi eQTLs om hese subpopula ions, e lec ing 3812 genes and 3650 SNPs. 25% o
he whole blood eQTLs ha e a signi ican (FDR ≤0.05) CTi eQTL e ec gi en Decon-
eQTL. The majo i y (31%) o he o al CTi eQTL e ec s de ec ed we e ound o be as-
socia ed o g anulocy e p opo ions, possibly because g anulocy es comp ise ~ 70% o
ci cula ing whi e blood cells (Fig. 3a). The majo i y (74%) o CTi eQTLs de ec ed by
ou me hod we e assigned o a single cell ype (Supplemen a y Fig. 6A). Simila ly, we
ind almos no sha ing be ween cell ypes in single-cell eQTLs om 112 indi iduals.
Howe e , i should be no ed ha hese eQTLs a e likely no exclusi ely p esen o his
pa icula cell ype in biology, bu ha he s a is ical powe gi en ou sample size was
su icien o de ec he in e ac ion e ec s ha we desc ibe as CTi eQTL in his pa icu-
la cell ype. Decon-eQTL was only able o ind a ew cases o sha ing o CTi eQTLs
be ween cell ypes, likely due o a lack o powe o he in e ac ion model. An example
Fig. 3 Decon olu ion o whole blood eQTLs in o CTi eQTLs. Decon-eQTL de ec s CTi eQTLs by in eg a ing
p opo ions o cell subpopula ions (p edic ed by Decon-cell), gene exp ession and geno ype in o ma ion. a
Numbe o decon olu ed CTi eQTLs in each cell ype using whole blood RNA-seq da a o 3189 samples in
BIOS coho . bDis ibu ion o Spea man co ela ion coe icien s be ween exp ession le els o CTi eQTL
genes and cell coun s o each cell subpopula ion. The CTi eQTL genes show posi i e and s a is ically
highe co ela ion (Spea man) wi h he ele an cell ype p opo ions as compa ed o he es (T- es p-
alue < 0.05) in an independen coho (500FG)
Agui e-Gamboa e al. BMC Bioin o ma ics (2020) 21:243 Page 7 o 23
o such a sha ed CTi eQTLs can be seen o he NOD2 gene, whe e Decon-eQTL de-
ec ed a s ong g anulocy e-eQTL e ec alongside a smalle opposi e e ec in CD14+
monocy es. This opposi e e ec has also been p e iously desc ibed in eQTL s udies on
pu i ied CD14+ monocy es and neu ophils [8]. These esul s demons a e ha he e -
ec s o cell p opo ions on gene exp ession should be aken in o accoun when in e -
p e ing eQTLs de i ed om bulk issues.
Decon-eQTL p io i izes genes o ele an cell ypes
CTi eQTL genes a e expec ed o ha e highe exp ession le els in hei ele an cell
ypes, and hei exp ession in whole blood should he e o e be co ela ed wi h he
p opo ions o hese ele an cell ypes. To es his, we e alua ed i he exp ession
le els o he CTi eQTL genes de ec ed in heBIOScoho we eco ela edwi h
hei ele an cell p opo ions, and compa ed his o he co ela ion wi h non-
ele an cell ypes. We calcula ed he Spea man co ela ion coe icien s be ween
he exp ession o he iden i ied CTi eQTL genes and he measu ed cell p opo ions
in he 500FG coho (n= 89). We hen compa ed he co ela ion coe icien s we
ob ained he e wi h hose be ween exp ession and he emaining cell p opo ions.
Fo each o he six cell subpopula ions we e alua ed in Decon-eQTL, hei CTi
eQTL genes had a signi ican ly highe co ela ion wi h hei ele an cell subpopu-
la ion han wi h o he cell ypes (T- es , p- alue < 0.05) (Fig. 3b). As such, his e-
sul shows a signi ican associa ion be ween CTi eQTL genes and he p opo ion
o hei ele an CT in an independen coho .
Nex , we e alua ed whe he he signi ican CTi eQTL genes we e o e -exp essed in
hei ele an cell subpopula ion compa ed o eQTL genes ha we e ound o be non-
signi ican CTi eQTLs o he same cell ype. Fo his pu pose, we made use o he pu i-
ied neu ophil, CD14+ monocy e, CD4+ T-cell and B-cell RNA-seq da a om he BLUE-
PRINT da ase . We include hese cell ypes because hey we e he only ones wi h mo e
han h ee samples measu ed. Fo each o he ou cell ypes, we obse ed ha he exp es-
sion o CT eQTL genes de ec ed by Decon-eQTL was signi ican ly highe (T- es , p- alue
≤0.05) han he exp ession o non-signi ican Decon-eQTL genes (Fig. 4a). We also ob-
se ed ha he decon olu ed eQTL genes om g anulocy es showed a ela i ely wide
ange o a ia ion han he CT eQTL genes om he o he h ee subpopula ions. We hy-
po hesized ha his could be explained by he ac ha g anulocy es comp ise ~ 70% o
he cell composi ion in whole blood, hus gi ing us he powe o de ec eQTL o lowly
exp essed genes in g anulocy es. This is pa ly suppo ed by he obse a ion ha he a i-
a ion o exp ession in whole blood o g anulocy e CTi eQTL genes was signi ican ly
g ea e han o hose CTi eQTL genes decon olu ed o he o he i e cell subpopula ions
(F- es , p- alue ≤0.05, Supplemen a y Fig. 7).
Fu he mo e, by using publicly a ailable ansc ip ome p o iles (GSE78840 [27]) o
pu i ied NK cells and CD4+ T cells, we assessed i he di e en ially exp essed genes
ac oss he wo cell ypes we e en iched o eGenes o decon olu ed CT eQTLs. He e
we obse ed ha he CD4+ di e en ially exp essed genes (Adjus ed P- alue ≤0.05)
we e signi ican ly en iched o CD4+ T cell eQTLs (Fishe exac P= 1.8 × 10
−17
),
whe eas NK cell di e en ial genes (Adjus ed P- alue ≤0.05) we e signi ican ly en iched
o NK cell eQTLs (Fishe exac P= 2.3 × 10
−18
) as shown in Fig. 4b.
Agui e-Gamboa e al. BMC Bioin o ma ics (2020) 21:243 Page 8 o 23
In summa y, we we e able o show ha he eQTL genes de ec ed by Decon-eQTL
a e ansc ip ionally ac i e in hei ele an cell ype because ha is whe e hey a e
mo e highly exp essed.
CT eQTLs iden i ied by Decon-eQTL in whole blood a e eplica ed in pu i ied cell eQTL
da ase s
To alida e he CT eQTLs de ined by Decon-eQTL, we u ilized eQTLs iden i ied om
pu i ied neu ophils, CD4+ T-cells and CD14+ monocy es [9]. We i s compa ed he
absolu e e ec sizes o eQTLs om pu i ied cells ha a e also signi ican ly decon o-
lu ed CTi eQTLs o he e ec sizes o eQTLs om pu i ied cells ha a e also non-
signi ican decon olu ed CTi eQTLs o his cell ype. Fo all h ee cell popula ions, e -
ec sizes in ou decon olu ed CTi eQTLs we e signi ican ly highe han he e ec sizes
o eQTLs wi hou a signi ican CTi eQTL (Wilcoxon es , p- alue ≤0.05, Fig. 4c). Nex ,
we assessed he speci ici y o ou decon olu ed CTi eQTLs by e alua ing CTi eQTL e -
ec sizes in non- ele an cell subpopula ions. Fo example, we compa ed he e ec
sizes o decon olu ed g anulocy e CTi eQTLs agains hose wi h non-signi ican decon-
olu ed g anulocy e CTi eQTLs using he e ec sizes o pu i ied CD4+ T-cell eQTLs.
No ably, we obse ed no s a is ically signi ican di e ences using e ec sizes om non-
ele an cell subpopula ions (see o -diagonal compa isons in Supplemen a y Fig. 8),
which u he suppo s he biological ele ance o ou decon olu ed CTi eQTLs. How-
e e , when compa ing he e ec sizes in he pu i ied eQTLs o only he CTi eQTLs ha
Fig. 4 Valida ion o CTi eQTLs. aThe exp ession o CTi eQTL genes in pu i ied cell subpopula ions om
BLUEPRINT [23] a e signi ican ly highe in he ele an cell subpopula ion when compa ed o o he a ailable
cell sub ypes (g een o g anulocy e eQTL genes showing exp ession o pu i ied neu ophils; o ange o
monocy es; pu ple o CD4+ T cells; pink o B cells). bGenes di e en ially exp essed (Adjus ed p- alue
≤0.5) be ween CD4+ T cells and NK cells a e signi ican ly en iched o CT eQTLs e ec s on CD4+ T cells
(do s in pu ple, Fishe exac P= 1.8 × 10
17
) and NK Cells (do s in yellow, Fishe exac P= 2.3 × 10
18
),
espec i ely. cCTi-eQTLs (FDR ≤0.05) show signi ican ly la ge e ec sizes in he pu i ied cell eQTL da a [9]
compa ed o he es o he whole blood eQTLs o which we do no de ec a cell ype e ec , as shown o
decon olu ed g anulocy e eQTLs in neu ophil-de i ed eQTLs (g een),monocy es (o ange) and CD4+ T
cells (pu ple)
Agui e-Gamboa e al. BMC Bioin o ma ics (2020) 21:243 Page 9 o 23
geno ypes and exp ession da a is a ailable and could po en ially aid in unde s anding
he molecula e ec s o gene ic isk ac o s associa ed wi h complex diseases a he
cell-subpopula ion le el. Ou me hod makes i possible o c ea e CT gene egula o y
ne wo ks ha could explain he di e en e ec s ha each CT has on a complex disease
in a cos -e icien way. Since Decon2 only equi es gene exp ession and geno ype in o -
ma ion o decon olu e bulk blood eQTLs in o CTi eQTLs, i is possible o e-analyze
exis ing bulk blood RNA-seq da a o which geno ypes a e also a ailable. In his sce-
na io, we would use Decon-cell o p edic cell p opo ions in whole blood and ob ain
CT in o ma ion on many mo e eQTLs h ough an inc ease in sample size. In addi ion
o whole blood, he me hods behind Decon2 can po en ially be gene alized o use an-
sc ip ional p o iles de i ed om any o he ype o bulk issue, such as biopsies om u-
mo s o o he solid issues implica ed in complex disease e iology. Howe e , he
me hod has no ye been es ed in o he issues. Ou me hods can hence aid in he de-
ec ion o gene ic e ec s on gene exp ession in a e cell subpopula ions in bulk issues.
Me hods
RNA-seq da a collec ion in 500FG coho
We selec ed a ep esen a i e subse o 89 samples om he 500 pa icipan s o he
500FG coho , which is pa o he Human Func ional Genomics P ojec (HFGP). Ou
subse was balanced o age and sex based on he o iginal dis ibu ion in he coho .
RNA was isola ed om whole blood and globin ansc ip s we e subsequen ly il e ed
by applying he Ambion GLOBINclea ki . The samples we e hen p ocessed o se-
quencing using he Illumina T uSeq 2.0 lib a y p epa a ion ki . Pai ed-end sequencing
o 2 × 50-bp eads was pe o med on he Illumina HiSeq 2000 pla o m. The quali y o
he aw eads was checked using Fas QC (h p://www.bioin o ma ics.bab aham.ac.uk/
p ojec s/ as qc/). Read alignmen was pe o med wi h STAR 2.3.0 [32,33] using he hu-
man Ensembl GRCh37.75 as e e ence, and he aligned eads we e so ed using SAM-
Tools [34]. Las ly, gene-le el quan i ica ion o he eads was done using HTSeq [35].
RNA-seq p epa a ion and da a p ocessing in he BIOS coho
RNA was isola ed om whole blood and globin ansc ip s we e subsequen ly il e ed
by applying he Ambion GLOBINclea ki . Lib a y p epa a ion was pe o med using
he Illumina T uSeq 2 lib a y p epa a ion ki . Nex , Illumina HiSeq 2000 was used o
pai ed-end sequencing o 2 × 50 bp eads while pooling 10 samples pe lane and expec -
ing > 15 million ead pai s pe sample. Read se s we e gene a ed using CASAVA,
e aining only hose eads ha passed Illumina Chas i y Fil e .
Quali y con ol o he eads was e alua ed using Fas QC (h p://www.bioin o ma ics.
bab aham.ac.uk/p ojec s/ as qc/). Adap o sequences we e immed ou using cu adap
( 1.1) wi h de aul se ings. Low quali y ends o eads we e emo ed using Sickle
( 1.200) (h ps://gi hub.com/najoshi/sickle).
Reads we e hen aligned using STAR 2.3.0e [33]. All SNPs p esen in he Genome o
he Ne he lands (GoNL) wi h MAF ≥0.01 we e masked om he eads o a oid e e -
ence mapping bias. Read pai s wi h a mos eigh misma ches and mapping o a mos
i e posi ions we e used. Quan i ica ion o coun s pe genes was done using Ensembl
.71 anno a ion (which co esponds o GENCODE .16).
Agui e-Gamboa e al. BMC Bioin o ma ics (2020) 21:243 Page 16 o 23

Geno ype da a o he BIOS coho
Geno ype in o ma ion was independen ly gene a ed o each o he coho s, u he de-
ails on da a collec ion and me hods used o geno yping can be ound in hei pape s
(CODAM [36], LLDeep [16], LLS [17], RS [18] and NTR [21]).
Geno ypes we e ha monized o GoNL wi h Geno ype Ha monize [37] and impu ed
wi h IMPUTE2 [38] using GoNL as e e ence panel. SNPs wi h an impu a ion sco e
below 0.5, a Ha dy-Weinbe g equilib ium P- alue smalle han 1 × 10
−4
, a call a e
below 95%, o a MAF smalle han 0.05 we e il e ed ou . Fo u he analysis, only
eSNPs om whole blood cis-eQTL op e ec s we e subsequen ly used in Decon-eQTL.
Quan i ica ion o cell p opo ions in 500FG coho
Inclusion c i e ia and u he desc ip ion o he pa icipan s o he 500FG coho can
be ound a h p://www.human unc ionalgenomics.o g. A o al o 73 manually anno-
a ed immune cell subpopula ions we e quan i ied using 10-colo low cy ome y. To
minimize biological a iabili y, cells we e p ocessed immedia ely a e blood sampling
and ypically analyzed wi hin 2–3 h. Cell popula ions we e ga ed manually as p e iously
desc ibed [14].
Cis-eQTLs in he BIOS coho
Fo cis-QTL mapping, we es ed o associa ion be ween genes and SNPs loca ed wi hin
250 kb o a gene cen e . SNPs wi h MAF ≥0.01, call a e = 1 and Ha dy-Weinbe g equi-
lib ium p- alue ≥0.0001 we e included. eQTLs we e decla ed o be signi ican a FDR <
0.05. P e-p ocessing o RNA-seq and QTL mapping was pe o med using a cus om
eQTL pipeline ha has been desc ibed p e iously [11].
No maliza ion and co ec ion o gene exp ession da a o decon olu ion o eQTL e ec s
To al ead coun s om HTSeq we e i s no malized using he immed means o M
(TMM) alues32. TMM exp ession alues we e hen log2 ans o med. Fo p edic ing
cell p opo ions, we used scaled exp ession da a in bo h he 500FG and BIOS coho s.
Fo he decon olu ion o eQTLs, he exp ession was log2 ans o med and co ec ed
o he e ec s o coho , age, sex, GC con en , RNA deg ada ion a es, lib a y size and
numbe o de ec ed genes pe sample using a linea model. The co ec ed exp ession
da a was hen exponen ia ed o main ain he o iginal linea ela ionship ac oss ead
coun s (gene exp ession) and cell p opo ions.
Gene al desc ip ion o Decon2
Decon2 is a s a is ical amewo k o es ima ing cell coun s using molecula p o iling
such as exp ession da a om he e ogeneous samples (Decon-cell) and consecu i e de-
con olu ion o exp ession quan i a i e ai loci (Decon-eQTL) in o each cell subpopu-
la ion. To p edic cell p opo ion le els using Decon-cell buil in models, i ’s only inpu
is a ma ix As inpu Decon-cell akes a able o no malized gene exp ession coun s,
wi h samples as columns and genes as ows, and ou pu s a able o p edic ed cell coun
p opo ions o cell ypes ha we e included in he aining model.. Decon-cell also en-
ables he use o gene a e i s own cus om models, o which i equi es a ma ix o gene
exp ession o ain he model and a ma ix o measu ed cell p opo ions; his will
Agui e-Gamboa e al. BMC Bioin o ma ics (2020) 21:243 Page 17 o 23
ou pu a lis wi h one speci ic model o each o he cell ypes included. A ma ix able
o no malized gene exp ession le elscoun s, a ma ix able o p edic ed o measu ed cell
coun p opo ions, and a ma ix able o geno ype dosages (0 o homozygous e e ence,
1 o he e ozygous, and 2 o homozygous al e na i e), las lyand a able wi h he SNP +
gene combina ions o es , a e used as inpu o Decon-eQTL, and his ou pu s o each
SNP + gene combina ion he be a and p- alue o he cell- ype dependen eQTL e ec .
See supplemen al Fig. 20 o a g aphical o e iew.
P edic ion o cell p opo ions using gene exp ession le els om bulk issue (Decon-cell)
Fo cell coun p edic ion, exp ession da a is TMM no malized, log2(exp ession+ 1)
ans o med and z- ans o med (scaled). We p oposed ha he abundance o molecula
ma ke s such as gene exp ession could be used as p oxies o p edic cell p opo ions.
This can be ep esen ed as:
Ckj ¼βki Yij þekj ð1Þ
whe e exp ession da a is Y
ij
o genes i=1, 2,…, G and samples j=1, 2, …,Nand cell
coun da a is C
kj
o sample jin cell ype k(k = 1, 2, …, K). β
ki
ep esen s he coe i-
cien s o gene iin de e mining cell coun s o cell ype ko a complex issue. e
kj
is he
e o e m.
In o de o selec only he mos in o ma i e genes o p edic ing cell coun s, we im-
plemen ed a ea u e selec ion scheme by applying an elas ic ne (EN) egula ized e-
g ession [26]. In he EN algo i hm, he β
k
Ya e es ima ed by minimizing:
Ck−βkY



2subjec o 1−αðÞβk



2þαβ
k


1≤sð2Þ
sis a uning pa ame e ha limi s he numbe o ea u es ha will be included in he
inal p edic o model. We es ima e he bes spe cell ype by applying a 10- old c oss-
alida ion app oach, whe e he mos op imal penal y pa ame e (α) was ob ained.
Decon olu ion o eQTL e ec s (Decon-eQTL)
Decon-eQTL models he exp ession le el in he bulk issue by conside ing he gene ic
con ibu ion o mul iple cell ypes p esen in he sys em. Fo iden i ying he CT eQTL
e ec , he in e ac ion e m be ween a pa icula cell ype and geno ype was es ed o
s a is ically signi ican con ibu ion o he explained a iance on he exp ession le els
o pa icula genes, while accoun ing o he emaining cell p opo ions. I we conside
a gene ic eQTL linea model o whole blood i can be desc ibed as:
y¼aþβ:gþeð3Þ
whe e yis he measu ed gene exp ession, a he modeled non-gene ic dependen ex-
p ession, g he geno ype coded as 0, 1 o 2, β.g he geno ype-dependen exp ession and
e he e o , e.g. unknown en i onmen al e ec s. He e, all h ee e ms a e modeling he
e ec o he mix u e o di e en cell ypes p esen in blood. In an RNA-seq-based gene
exp ession quan i ica ion o a bulk issue, one could exp ess gene exp ession le els (y)
as he sum o coun s (ψ) pe Kcell ypes:
Agui e-Gamboa e al. BMC Bioin o ma ics (2020) 21:243 Page 18 o 23
y¼XK
k¼1ψkð4Þ
Fo e e y cell ype, he exp ession le el can be w i en as a gene ic eQTL model (eq. 3)
weigh ed by he cell p opo ions. ψ
k
is a combina ion o he gene ic and non-gene ic con-
ibu ion o he cell ype o y. The non-gene ic con ibu ion pe cell ype is β.c,whe ecis
he cell coun p opo ions. The gene ic con ibu ion is β
k
.g:c
k
.Fo kcell ypes he ex-
p ession is hen:
y¼XK
k¼1ψk¼Σk:βk:ck

þΣk:γk:gck

þeð5Þ
whe e yis he measu ed exp ession le els, kis he o al numbe o cell ypes, c
k
is he
cell coun p opo ions o cell ype k,gis he geno ype and eis he e o e m. Since we
a e assuming a linea ela ionship be ween o al gene exp ession and he le els o ex-
p ession gene a ed by each o he cell ypes composing a bulk issue, he cell p opo -
ions a e scaled o sum o 100% such ha he sum o he e ec o he cell ypes equals
he e ec in whole blood. He e we assume ha he ue sum o he cell coun s should
be e y close o 100% o he o al PBMC coun , which is why we include he 6 cell
ypes ha oge he o m he op hie a chy gi en he ga ing s a egy used o quan i y
he cell subpopula ions [14]. The geno ype main e ec is no included in he model be-
cause he sum o he geno ype e ec pe cell ype should app oxima e he main e ec .
Because he con ibu ion o each o he cell ypes o exp ession le el ycanno be
nega i e, we cons ain he e ms o he model o be posi i e using Non-Nega i e Leas
Squa es [39,40] o i he pa ame e s o he measu ed exp ession le els. Howe e , i
he allele ha has a nega i e e ec on gene exp ession is coded as 2, he bes i would
ha e a nega i e in e ac ion e m, which would be se o 0. To add ess his, we wan he
allele ha causes a posi i e e ec on gene exp ession o always be coded as 2. Howe e ,
he e ec o an allele can be di e en pe cell ype, he e o e he coding o he SNP
should also be di e en pe cell ype. We he e o e un he model mul iple imes, swap-
ping he geno ype encoding o one o he in e ac ion e ms each ime. The encoding
ha gi es he lowes R-squa ed is hen chosen as he op imal geno ype encoding. Fo
he encoding, we limi he numbe o geno ypes ha ha e an opposi e geno ypic encod-
ing o a maximum o one in e ac ion e m, as we ha e obse ed ha his leads o no
signi ican di e ence when compa ed o using all possible con igu a ions and limi s he
numbe o models ha ha e o be un om k
2
o (2*k) + 2.
To es i he e is a CT in e ac ion e ec , we un he linea model o eq. 5and, o
each CT, un he same model wi h he cell p opo ion:geno ype in e ac ion e m e-
mo ed. Fo example, when es ing wo cell ypes he ull model is:
y¼β1:c1þβ2:c2þγ1:gc1þγ2:gc2þeð6Þ
and he wo models wi h he in e ac ion e ms emo ed a e:
y¼β1:c1þβ2:c2þγ1:gc1þe
y¼β1:c1þβ2:c2þγ2:gc2þeð7Þ
Fo bo h he ull model and he CT models, we calcula ed he sum o squa es using
he di e en geno ype con igu a ions de ailed abo e. Fo bo h he ull and he CT
models, we hen selec ed he geno ype con igu a ion wi h lowes sum o squa es. Then,
Agui e-Gamboa e al. BMC Bioin o ma ics (2020) 21:243 Page 19 o 23
o each CT, we es ed i he ull model could signi ican ly explain mo e a iance han
he CT model using an ANOVA.
We hen applied ou s a egy o 16,362 signi ican whole blood cis-eQTL op e ec s
de ec ed using he BIOS coho . We hen co ec he p- alues o mul iple es ing using
FDR o each o he cell ypes, i.e. g anulocy e eQTL p- alues we e co ec ed o 16,
362 es s in he same way as CD4+ T cells eQTL p- alues we e co ec ed o he exac
same numbe o es s.
Wes a e al. in e ac ion model
In he Wes a e al. model, exp ession da a is no malized in he same way as in Decon-
eQTL. The e ec o he cell ype is p edic ed using a geno ype * cell coun in e ac ion e m:
y¼Iþβ1:Gþβ2:cþβ3:cxGþe
whe e y is exp ession, I he in e cep , G he geno ype, c he cell coun and c x G he cell
coun x geno ype in e ac ion e m. Addi ional es ic ions a e se on he p- alues. Fo
neu ophils, i ( he βo he neu ophil xGin e ac ion e m) * ( he βo he Gin e ac ion
e m) < 0, he p- alue is se o 1. Fo CD4+ and monocy es, i ( he βo he neu ophil xG
in e ac ion e m) * ( he βo he Gin e ac ion e m) > 0, he p- alue is se o 1.
Compa ison be ween allelic conco dance
Fo he compa ison be ween allelic conco dances, we coun ed he conco dan and dis-
co dan eQTLs o each o he cell ype compa isons and did a Fishe exac es be-
ween each o he g oups. The p- alues a e Bon e oni-co ec ed.
Single-cell eQTLs
The sc-eQTLs we e ob ained o 112 indi iduals in he same way as desc ibed in Van
de Wijs e al. [24] Fo he allelic di ec ion compa ison, we used all signi ican eQTLs.
Classical monocy e and non-classical monocy e eQTLs we e combined and join ly
compa ed o Decon-eQTL Monocy es.
Supplemen a y in o ma ion
Supplemen a y in o ma ion accompanies his pape a h ps://doi.o g/10.1186/s12859-020-03576-5.
Addi ional ile 1 : Supplemen a y Figu e 1: P edic ion pe o mance o Decon-cell wi hin 500FG: The Y-axis ep-
esen s he 73 immune cell ypes quan i ied by FACS in he 500FG coho . The ba plo on he le panel shows
he mean P edic ion Pe o mance (Spea man co ela ion coe icien be ween p edic ed and measu ed cells ac oss
100- old c oss alida ions). On he igh panel, box plo s ep esen he dis ibu ion o he P edic ion Pe o mance
wi hin 100 i e a ions o he c oss alida ions. A cu o o mean P edic ion Pe o mance ≥0.5 was applied o de ine
p edic able cell ypes (g een). Supplemen a y Figu e 2. Signa u e genes selec ed o p edic ion o cell p opo -
ions by Decon-cell: (A) To al numbe o ma ke genes (genes selec ed in ≥80% o all models in he 100 i e a ions)
pe p edic able cell ype. Di e en colo s indica e di e en subpopula ions. (B) The numbe o genes signi ican ly
co ela ed wi h cell coun s (Spea man co ela ion, adjus ed P≤0.05) (y-axis) shows he o al numbe o signi ican ly
co ela ed genes, while he x-axis shows he p edic ion pe o mance (x-axis). (C) Dis ibu ions o he o al numbe
o “s ongly”co ela ed genes (absolu e Spea man co ela ion ≥0.3) be ween p edic able and unp edic able cell
subpopula ions. Supplemen a y Figu e 3. Compa ison o p edic ion pe o mance be ween Decon-cell and o he
exis ing me hods. (A) Pe o mance o Decon-cell: he measu ed (x axis) and p edic ed cell p opo ions (y-axis) we e
compa ed o neu ophils (gi en by g anulocy es in 500FG), lymphocy es and monocy es CD14+ and g anulocy es
in h ee independen coho s (shown by ow, om op o bo om: LLDeep (n= 627); LLS (n= 660); RS (n= 773)). (B)
Compa ison o p edic ion pe o mance o Decon-cell, CIBERSORT and xCell in h ee independen coho s o a
o al o 4 majo immune subpopula ions. Supplemen a y Figu e 4. P edic ion pe o mance o xCell and CIBER-
SORT in h ee independen Du ch popula ions (LLDeep, n = 627; LLS, n = 660; RS, n = 773).(A) Sca e plo s show-
ing he measu ed cell p opo ions o ci cula ing immune cells on he x-axis and he xCell en ichmen sco e on he
y-axis. (B) Sca e plo s showing he measu ed cell p opo ions o ci cula ing immune cells on he x-axis and he
p edic ed cell p opo ions gi en by CIBERSORT) on he y-axis. Supplemen a y Figu e 5. Exp ession o ma ke
Agui e-Gamboa e al. BMC Bioin o ma ics (2020) 21:243 Page 20 o 23
genes selec ed by Decon-cell. Exp ession le els (scaled, log2(TPM + 1)) o signa u e genes in he da a in h ee pu i-
ied cell subpopula ions: CD4+ T cells (A), neu ophils/g anulocy es (B) and monocy es (C) in he da a om BLUE-
PRINT. Cell subpopula ions a e indica ed in di e en colo s by columns. Co ela ion o each o he signa u e genes
and he cell subpopula ion pe cen age in he 500FG coho is shown on by he g een ba a he le -hand side o
hea map igu e, i.e. da ke g een co esponds o highe co ela ions. Supplemen a y Figu e 6. Many o he CTi
eQTL a e cell ype exclusi e. Colo ed ba plo on he le shows he o al numbe o signi ican CTi eQTLs in whole
blood eQTLs (as also shown in Fig. 2a). G ay ba plo shows he o al numbe o eQTLs sha ed ac oss he possible
combina ions o he six cell subpopula ions unde s udy. Supplemen a y Figu e 7. Va ia ion o gene exp ession
ac oss samples o decon olu ed cell- ype eQTLs genes in whole blood. G anulocy e eQTL genes show signi ican ly
highe a iance ac oss he BIOS samples (F es p- alue ≤0.05) compa ed o hose om monocy es, CD4+ T cells,
CD8+ T cells, B cells and NK cells. Supplemen a y Figu e 8. Valida ion o CTi eQTLs using e ec sizes o eQTLs
om pu i ied cells. CTi eQTLs (FDR ≤0.05) om he BIOS coho show a signi ican ly bigge e ec size in pu i ied
cell eQTLs [9] om hei ele an cell sub ype as compa ed o o he whole blood eQTLs (diagonal boxed compa i-
sons). The o -diagonal compa isons show ha hese eQTL genes a e speci ic o a cell subpopula ion because he
di e ences in e ec sizes a e non-signi ican in all bu one case (CD4+ T cell eQTL genes in monocy e-de i ed
eQTLs). Supplemen a y Figu e 9. Valida ion o CTi eQTLs using e ec sizes o K27AC QTLs om pu i ied cells. CTi
eQTLs (FDR ≤0.05) show a signi ican ly bigge e ec size o K27AC QTLs ha ha e peaks loca ed in he p omo e
egion o he eGenes om hei ele an cell sub ype compa ed o he es o he signi ican whole blood eQTLs
(diagonal boxed compa isons). The o -diagonal compa isons show ha hese eQTL genes a e speci ic o a cell sub-
ype because he di e ences in e ec sizes a e non-signi ican in all bu he compa isons ac oss Neu ophils and
Monocy es (CD14+). Supplemen a y Figu e 10. Valida ion o CTi eQTLs using e ec sizes o K4ME1 QTLs om
pu i ied cells. CTi eQTLs (FDR ≤0.05) show a signi ican ly bigge e ec size o K4ME1 QTLs (whe e he eGenes is
he closes gene agging he K4ME1 QTLs peak) om hei ele an cell sub ype compa ed o he es o he signi i-
can whole blood eQTLs (diagonal boxed compa isons). The o -diagonal compa isons show ha hese eQTL genes
a e speci ic o a cell sub ype because he di e ences in e ec sizes a e non-signi ican in all bu he compa isons
be ween neu ophils and monocy es (CD14+). Supplemen a y Figu e 11. Valida ion o CTi eQTLs using allelic
conco dance wi h eQTLs esul s om pu i ied cells. CTi eQTLs (FDR ≤0.05) show high allelic conco dance wi h hei
espec i e pu i ied cell eQTLs. Top ow shows allelic conco dance o decon olu ed g anulocy e eQTLs (all in g een)
agains neu ophils, monocy es and CD4+ T cells. Second ow shows decon olu ed monocy e eQTLs agains pu i-
ied cell eQTLs in he same o de as he op ow. Bo om ow shows he same compa isons as o decon olu ed
CD4+ eQTLs. Allelic conco dance o he o -diagonal (compa ing CTi eQLTs wi h non- ele an cell ypes) show a
consis en dec ease in allelic conco dance. P- alues a e Bon e oni-co ec ed Fishe exac es s be ween g oups.
Supplemen a y Figu e 12. Valida ion o CTi eQTLs using allelic conco dance wi h K27AC esul s om pu i ied
cells. CTi eQTLs (FDR ≤0.05) show a high allelic conco dance in hei espec i e pu i ied cell K27AC QTLs. Top ow
shows allelic conco dance o decon olu ed g anulocy e eQTLs (all in g een) agains neu ophils, monocy es and
CD4+ T cells de i ed om K27AC QTLs. Second ow shows decon olu ed monocy e eQTLs (all in o ange) agains
pu i ied cell K27AC QTLs in he same o de as op ow. Bo om ow shows he same compa isons as o decon o-
lu ed CD4+ eQTLs (all in pu ple). Allelic conco dance o he o -diagonal (compa ing decon olu ed eQTLs wi h
non- ele an cell ypes) show a consis en dec ease in allelic conco dance when compa ed o he ele an cell ype
compa isons. P- alues a e Bon e oni-co ec ed Fishe exac es s be ween g oups. Supplemen a y Figu e 13. Al-
lelic conco dance be ween whole blood eQTLs and K27AC QTLs o pu i ied neu ophils, CD14+ monocy es and
CD4+ T cells. Supplemen a y Figu e 14. Compa ison o whole blood eQTLs wi h eQTLs om single cell RNA-seq
Whole blood eQTLs show 89% allelic conco dance o signi ican eQTLs de i ed om scRNA-seq da a, comp ising
monocy es CD14+, B cells, CD4+ T cells, CD8+ T cells and NK cells. Supplemen a y Figu e 15 Valida ion o cell
ype eQTLs de ec ed in he BIOS coho using he Wes a e al me hod: (A) Exp ession o eGenes in pu i ied cell
subpopula ions om BLUEPRINT (g een o g anulocy e eQTL genes showing exp ession o pu i ied neu ophils;
o ange o monocy es; pu ple o CD4+ T cells; pink o B cells). (B) CT eQTLs de ec ed by he Wes a me hod show
a signi ican ly la ge e ec size in pu i ied cell eQTLs [11] as compa ed o he es o he whole blood eQTLs.
Boxed-diagonal shows he compa isons wi h ele an cell ypes whe e he e ec di e ences a e s onge . Supple-
men a y Figu e 16 Allelic conco dance a es o cell ype eQTLs de ec ed using he Wes a e al me hod and
eQTLs om pu i ied cells. Top ow shows allelic conco dance o g anulocy e CT eQTLs agains neu ophils, mono-
cy es and CD4+ T cells. Second ow shows CT monocy e eQTLs agains pu i ied cell eQTLs in he same o de as
op ow. Bo om ow shows he same compa isons o CT CD4+ eQTLs. Supplemen a y Figu e 17 Compa ison
o Decon-eQTL wi h Wes a e al me hod. O e lap o CT eQTLs de ec ed wi h Decon-eQTL and he Wes a e al
me hod and hose ound o be signi ican in pu i ied cell subpopula ions o g anulocy e QTLs (A), CD4+ T cells (B),
and monocy es (C). Supplemen a y Figu e 18 Dis ibu ion and co ela ion among ci cula ing cell p opo ions.
(A) Sca e plo s show he co ela ions be ween di e en cell subpopula ions in 89 samples om 500FG. Blue line
indica es a i ed linea model. Diagonal plo s depic he o e all densi y dis ibu ion pe cell ype. Uppe igh i-
angle shows he Pea son co ela ion coe icien o each pai wise compa ison. (B) Co ela ions be ween di e en
cell subpopula ions in he BIOS coho ob ained by p edic ion using Decon-cell. Supplemen a y Figu e 19.Gen-
e al o e iew o he Decon2 me hod. (A) Gene exp ession can be used o p edic cell coun pe cen ages o cell
coun s ha a e al eady ained in he Decon-Cell model. Addi ionally, he model can be ained on di e en cell
ypes i exp ession da a and cell coun p opo ions a e a ailable. (B) Decon-eQTL models he cell ype dependen
eQTL e ec using exp ession, geno ype, and measu ed cell coun p opo ions o , i una ailable, p edic ed cell coun
p opo ions.
Addi ional ile 2 : Supplemen a y Table 1: Ensembl IDs and symbol names o he ma ke genes selec ed by
Decon-cell o he 34 p edic able ci cula ing immune cell p opo ions.
Addi ional ile 3 : Supplemen a y Table 2: Summa y s a is ics om Decon-eQTLs o he 16,362 whole blood
eQTLs.
Agui e-Gamboa e al. BMC Bioin o ma ics (2020) 21:243 Page 21 o 23

Abb e ia ions
eQTL: exp ession quan i a i e ai loci; CT: Cell ype; CTi: Cell ype in e ac ion; GWAS: Genome-wide associa ion
s udies; sc: single cell; GxE: Gene by en i onmen in e ac ion; TMM: T immed means o M
Acknowledgemen s
We hank K Mc In y e and J Senio o edi ing he inal ex . We hank T. Spenkelink o he DeconCell web ool
design. We hank he UMCG Genomics Coo dina ion cen e , he UG Cen e o In o ma ion Technology and hei
sponso s BBMRI-NL & Ta Ge o s o age and compu e in as uc u e, and he Cen e o In o ma ion Technology o he
Uni e si y o G oningen o hei suppo and o p o iding access o he Pe eg ine high pe o mance compu ing clus e .
Au ho s’con ibu ions
C.W., L.F. and YL ini ialized he s udy. Y.L. and L.F. di ec ed and supe ised he p ojec . Y.L. de eloped he s a is ical
amewo k, oge he wi h L.F.. R.A-G, N.K., L.F., and Y.L., pe o med da a analysis and in e p e a ion. J.D.T. was in ol ed
in he ini ial analysis. N.K. and R.A-G. made he so wa e and web ool. A. C, U.V., M. Z, X.C., O.B.B., Z.B., I.R.P., P.D., C.J.X.,
M.S., I.J. [1], S.W., I.J. [2], S.S., V.K., H.J.P.M.K., L.A.B.J., M.G.N., M.W., D.V., H.B., R.O. and C.W. con ibu ed o da a collec ion,
da a analysis and in e p e a ion. R.A-G, N.K., L.F., and Y.L. d a and e ise he manusc ip . All au ho s ha e ead and
app o ed he manusc ip .
Funding
L.F. is suppo ed by g an s om he Du ch Resea ch Council (ZonMW-VIDI 917.164.455 o M.S. and ZonMW-VIDI
917.14.374 o L.F.), and by an ERC S a ing G an , g an ag eemen 637640 (ImmRisk). Y.L. was suppo ed by an
ZonMW-O Road g an (91215206). The HFGP is suppo ed by a Eu opean Resea ch Council (ERC) Consolida o g an
(ERC 310372). This s udy was u he suppo ed by an IN-CONTROL CVON g an (CVON2012–03) and a Ne he lands
O ganiza ion o Scien i ic Resea ch (NWO) Spinoza p ize (NWO SPI 94–212) o M.G.N.; an ERC ad anced g an (FP/
2007–2013/ERC g an 2012–322698) and an NWO Spinoza p ize (NWO SPI 92–266) o C.W.; a Eu opean Union Se en h
F amewo k P og amme g an (EU FP7) TANDEM p ojec (HEALTH-F3–2012-305279) o C.W. and V.K.;. A CONACYT-I2T2
schola ship (382117) o R.A-G. The Biobank-Based In eg a i e Omics S udies (BIOS) Conso ium is unded by BBMRI-NL,
a esea ch in as uc u e inanced by he Du ch go e nmen (NWO 184.021.007). This wo k was suppo ed by Radboud
Uni e si y Medical Cen e Hypa ia Tenu e T ack G an (2018) o Y.L.
A ailabili y o da a and ma e ials
The decon olu ion summa y s a is ics a e made a ailable as supplemen a y able. In o ma ion on how o eques he
geno ype and RNAseq da a used o he eQTL calcula ion can be ound he e: h ps://www.bbm i.nl/acquisi ion-use-
analyze/bios. A subse o he single cell eQTLs is p elimina y da a o which a manusc ip is in p epa a ion, and will be
made a ailable a e publica ion o ha manusc ip . Con ac Lude F anke ([email p o ec ed]) o eques access o his
da a. The GEO accession code o he exp ession da a o 500FG is GSE134080.
E hics app o al and consen o pa icipa e
We ha e used exis ing and al eady published da a only. The e o e, we did no ge p io e hics app o al o consen o
pa icipa e.
Consen o publica ion
No applicable.
Compe ing in e es s
The au ho s decla e no compe ing in e es s.
Au ho de ails
1
Depa men o Gene ics, Uni e si y o G oningen, Uni e si y Medical Cen e G oningen, G oningen, he Ne he lands.
2
Depa men o Gene ics, Oncode Ins i u e, Uni e si y o G oningen, Uni e si y Medical Cen e G oningen, G oningen,
he Ne he lands.
3
Es onian Genome Cen e, Ins i u e o Genomics, Uni e si y o Ta u, Ta u, Es onia.
4
Cen e o
Indi idualised In ec ion Medicine (CiiM) & TWINCORE, join en u es be ween he Helmhol z-Cen e o In ec ion
Resea ch (HZI) and he Hanno e Medical School (MHH), Feodo -Lynen-S . 7, 30625 Hanno e , Ge many.
5
Uni e si y o
G oningen and Uni e si y Medical Cen e G oningen, Genomics Coo dina ion Cen e , G oningen, he Ne he lands.
6
Depa men o Labo a o y Medicine, Labo a o y o Medical Immunology, Radboud Uni e si y Medical Cen e,
Nijmegen, he Ne he lands.
7
Depa men o In e nal Medicine and Radboud Cen e o In ec ious Diseases, Radboud
Uni e si y Medical Cen e , Nijmegen, he Ne he lands.
8
Depa men o Genomics & Immuno egula ion, Li e and
Medical Sciences Ins i u e (LIMES), Uni e si y o Bonn, Bonn, Ge many.
Recei ed: 17 Janua y 2020 Accep ed: 1 June 2020
Re e ences
1. Hindo LA, e al. Po en ial e iologic and unc ional implica ions o genome-wide associa ion loci o human diseases
and ai s. P oc Na l Acad Sci U S A. 2009;106:9362–7.
2. Ioni a-Laza I, McCallum K, Xu B, Buxbaum JD. A spec al app oach in eg a ing unc ional genomic anno a ions o
coding and noncoding a ian s. Na Gene . 2016;48:214–20.
3. Ja ie e BM, e al. Lineage-Speci ic Genome A chi ec u e Links Enhance s and Non-coding Disease Va ian s o Ta ge
Gene P omo e s. Cell. 2016;167:1369–1384.e19.
4. Wes a H-J, e al. Sys ema ic iden i ica ion o ans eQTLs as pu a i e d i e s o known disease associa ions. Na Gene .
2013;45:1238–43.
Agui e-Gamboa e al. BMC Bioin o ma ics (2020) 21:243 Page 22 o 23
5. Joehanes R, e al. In eg a ed genome-wide analysis o exp ession quan i a i e ai loci aids in e p e a ion o genomic
associa ion s udies. Genome Biol. 2017;18:16.
6. Raj T, e al. Pola iza ion o he e ec s o au oimmune and neu odegene a i e isk alleles in leukocy es. Science. 2014;
344:519–23.
7. Pe e s JE, e al. Insigh in o Geno ype-Pheno ype Associa ions h ough eQTL Mapping in Mul iple Cell Types in Heal h
and Immune-Media ed Disease. PLoS Gene . 2016;12:e1005908.
8. Na anbhai V, e al. Genomic modula o s o gene exp ession in human neu ophils. Na Commun. 2015;6:7545.
9. Chen L, e al. Gene ic D i e s o Epigene ic and T ansc ip ional Va ia ion in Human Immune Cells. Cell. 2016;167:1398–
1414.e24.
10. Wes a H-J, e al. Cell Speci ic eQTL Analysis wi hou So ing Cells. PLoS Gene . 2015;11:e1005223.
11. Zhe nako a DV, e al. Iden i ica ion o con ex -dependen exp ession quan i a i e ai loci in whole blood. Na Gene .
2017;49:139–45.
12. A an D, Hu Z, Bu e AJ. xCell: digi ally po aying he issue cellula he e ogenei y landscape. Genome Biol. 2017;18:220.
13. Ne ea MG, e al. Unde s anding human immune unc ion using he esou ces om he Human Func ional Genomics
P ojec . Na Med. 2016;22:831–3.
14. Agui e-Gamboa R, e al. Di e en ial E ec s o En i onmen al and Gene ic Fac o s on T and B Cell Immune T ai s. Cell
Rep. 2016;17:2474–87.
15. Bakke OB, e al. In eg a ion o mul i-omics da a and deep pheno yping enables p edic ion o cy okine esponses. Na
Immunol. 2018;19:776–86.
16. Tigchelaa EF, e al. Coho p o ile: Li eLines DEEP, a p ospec i e, gene al popula ion coho s udy in he no he n
Ne he lands: s udy design and baseline cha ac e is ics. BMJ Open. 2015;5:e006772.
17. Deelen J, e al. Genome-wide associa ion me a-analysis o human longe i y iden i ies a no el locus con e ing su i al
beyond 90 yea s o age. Hum Mol Gene . 2014;23:4420–32.
18. Ho man A, e al. The Ro e dam S udy: 2014 objec i es and design upda e. Eu J Epidemiol. 2013;28:889–926.
19. an G ee enb oek MMJ, e al. The c oss-sec ional associa ion be ween insulin esis ance and ci cula ing complemen C3
is pa ly explained by plasma alanine amino ans e ase, independen o cen al obesi y and gene al in lamma ion ( he
CODAM s udy). Eu J Clin In es ig. 2011;41:372–9.
20. Schoenmake M, e al. E idence o gene ic en ichmen o excep ional su i al using a amily app oach: he Leiden
Longe i y S udy. Eu J Hum Gene . 2006;14:79–84.
21. Willemsen G, e al. The Ne he lands Twin Regis e biobank: a esou ce o gene ic epidemiological s udies. Twin Res
Hum Gene . 2010;13:231–45.
22. Bonde MJ, e al. Disease a ian s al e ansc ip ion ac o le els and me hyla ion o hei binding si es. Na Gene . 2017;
49:131–8.
23. Adams D, e al. BLUEPRINT o decode he epigene ic signa u e w i en in blood. Na Bio echnol. 2012;30:224–6.
24. an de Wijs MGP, e al. Single-cell RNA sequencing iden i ies cell ype-speci ic cis-eQTLs and co-exp ession QTLs. Na
Gene . 2018;50:493–7.
25. Newman AM, e al. Robus enume a ion o cell subse s om issue exp ession p o iles. Na Me hods. 2015;12:453–7.
26. F iedman J, Has ie T, Tibshi ani R. Regula iza ion pa hs o gene alized linea models ia coo dina e descen . J S a
So w. 2010;33:1–22.
27. G uden K, e al. A ‘c ossomics’s udy analysing a iabili y o di e en componen s in pe iphe al blood o heal hy
caucasoid indi iduals. PLoS One. 2012;7:e28761.
28. Da enpo EE, e al. Disco e ing in i o cy okine eQTL in e ac ions om a lupus clinical ial; 2017. h ps://doi.o g/10.
1101/118703.
29. Wilson DR, Sun W, Ib ahim JG. Mapping Tumo -Speci ic Exp ession QTLs In Impu e Tumo Samples; 2017. h ps://doi.
o g/10.1101/136614.
30. Geelehe P, e al. Cance exp ession quan i a i e ai loci (eQTLs) can be de e mined om he e ogeneous umo gene
exp ession da a by modeling a ia ion in umo pu i y. Genome Biol. 2018;19:130.
31. Glas onbu y CA, Al es AC, Mous a a JE-S, Small KS. Cell- ype he e ogenei y in adipose issue is associa ed wi h complex
ai s and e eals disease- ele an cell-speci ic eQTLs; 2018. h ps://doi.o g/10.1101/283929.
32. Robinson MD, McCa hy DJ, Smy h GK. edgeR: a bioconduc o package o di e en ial exp ession analysis o digi al
gene exp ession da a. Bioin o ma ics. 2010;26:139–40.
33. Dobin A, e al. STAR: ul a as uni e sal RNA-seq aligne . Bioin o ma ics. 2013;29:15–21.
34. Li H, e al. The Sequence Alignmen /Map o ma and SAM ools. Bioin o ma ics. 2009;25:2078–9.
35. Ande s S, Pyl PT, Hube W. HTSeq--a Py hon amewo k o wo k wi h high- h oughpu sequencing da a. Bioin o ma ics.
2015;31:166–9.
36. an Dam RM, Boe JM, Feskens EJ, Seidell JC. Pa en al his o y o diabe es modi ies he associa ion be ween abdominal
adiposi y and hype glycemia. Diabe es Ca e. 2001;24:1454–9.
37. Deelen P, e al. Geno ype ha monize : au oma ic s and alignmen and o ma con e sion o geno ype da a
in eg a ion. BMC Res No es. 2014;7:901.
38. Howie BN, Donnelly P, Ma chini J. A lexible and accu a e geno ype impu a ion me hod o he nex gene a ion o
genome-wide associa ion s udies. PLoS Gene . 2009;5:e1000529.
39. Zhou X, Song Y, Wang L, Liu Q. P econdi ioned GAOR me hods o sol ing weigh ed linea leas squa es p oblems. J
Compu Appl Ma h. (2009;224:242–9.
40. Lawson CL, Hanson RJ. Sol ing Leas Squa es P oblem; 1995.
Publishe ’sNo e
Sp inge Na u e emains neu al wi h ega d o ju isdic ional claims in published maps and ins i u ional a ilia ions.
Agui e-Gamboa e al. BMC Bioin o ma ics (2020) 21:243 Page 23 o 23