Download GWAS Identifies Classical HLA Alleles Associated with Susceptibility

Survey
yes no Was this document useful for you?
   Thank you for your participation!

* Your assessment is very important for improving the workof artificial intelligence, which forms the content of this project

Document related concepts

Sociality and disease transmission wikipedia , lookup

Neglected tropical diseases wikipedia , lookup

Rheumatic fever wikipedia , lookup

Infection control wikipedia , lookup

Major histocompatibility complex wikipedia , lookup

Globalization and disease wikipedia , lookup

HLA A1-B8-DR3-DQ2 wikipedia , lookup

Sjögren syndrome wikipedia , lookup

Germ theory of disease wikipedia , lookup

Infection wikipedia , lookup

Hygiene hypothesis wikipedia , lookup

Transmission (medicine) wikipedia , lookup

A30-Cw5-B18-DR3-DQ2 (HLA Haplotype) wikipedia , lookup

Human leukocyte antigen wikipedia , lookup

Transcript
GWAS Identifies Classical HLA
Alleles Associated with
Susceptibility to Infectious Diseases
Chao Tian1, Joanna L. Mountain1, Nicholas Eriksson1, Joyce Y. Tung1, Amy K. Kiefer1, David A. Hinds1!
123andMe,
Inc, Mountain View, CA;
Introduction
Results
Human Leukocyte Antigen (HLA) system, the major histocompatibility
complex(MHC) in humans, has long been known to play an important
role in susceptibility and resistance to many infectious diseases and
responsiveness to pathogens or vaccines. Most studies by far have
tested only candidate loci in small samples, and the associations
between HLA alleles and many infectious diseases are not well studied.
We conducted genome wide association studies on 23 infectious
diseases. We also imputed classic HLA alleles using HIBAG1 over all
23andMe customers to explore the relationship between individual HLA
alleles and susceptibility to infectious diseases. We identified single
nucleotide polymorphisms in the MHC region and HLA alleles that are
significantly associated with chicken pox, cold sores, childhood ear
infection, plantar warts, mumps, pneumonia, positive TB test, scarlet
fever, shingles and tonsillectomy. For most of these diseases, the most
significantly associated SNP is highly correlated with the top association
HLA allele (r2>0.95). The reconciliation of SNP and HLA allele
associations implicate the function of specific HLA peptides as the major
factors modulating infectious diseases. We did not detect associations
between HLA loci and bladder infection, hepatitis, chronic sinus
infection, colds last year, gingivitis, strep throat, urinary tract infection,
measles, intestinal parasite, myringotomy, mononucleosis, rubella or
rheumatic fever, some of which are probably due to small cohort sizes.!
We identified single nucleotide polymorphisms in the MHC region that
are significantly (p<5x10-8) associated with 10 of the 23 infectious
diseases. We also identified the either a susceptibility or resistance of
specific HLA alleles (p<1x10-5) to those infectious diseases(Table 2,
Figure1). The HLA alleles were imputed in all samples using HIBAG.
Similar to the SNP imputation, we measured the imputation quality using
the ratio of the empirically observed variance of the allele dosage to the
expected binomial variance p(1-p) at Hardy-Weinberg equilibrium, where
p is the observed allele frequency. In our data, all the top association
HLA has high imputation quality(Table 2).!
To determine whether the association detected for the top SNP and the
top HLA allele is related or independent, we performed reciprocal
regression tests and also examined the correlation (r2) between the SNP
and the HLA allele and generally found consistency between them. The
top SNP association is stronger than the top HLA association except
scarlet fever (Table 2). For scarlet fever and pneumonia, their top SNP
and top HLA allele signals are localized in different genes (Figure 1).!
Next we conditioned each disease dataset on the most significant
association to identify other signals independent of the top association.
For many of the diseases, there was evidence of multiple additional
association signals within MHC region (Figure 1).!
!"#$%#$&
Methods
:7"4;$.&+,<&
:,2P&#,5$#&
!
!"#$!%&$'()#$
$
*"#-#$
*0.)402$
*023$#04-#$
;<=>?$
<>;ED$
6.-9:0.("$
@A?;D$
E=AA@$
@>AA>$
E<>?<$
60#()(G-$HI$
>@?D$
;<>@;$
*'(23'003$
-"4$(.5-+)(0.$
@?<;?$
>=F@;$
J+"42-)$5-G-4$
=A@D$
DA?EF$
62".)"4$7"4)#$
89:/#$
$
*"#-#$
*0.)402$
$
*"#-#$
*0.)402$
FDF;$
;@F@?$
P.)-#)(."2$
/"4"#()-#$
??F@$
>E@D<$
I2"33-4$
(.5-+)(0.$
@===F$
E<AE@$
B)2$C$<>AAA$
@FA?;$
J'(.K2-#$
<<=F;$
;;EE?$
@DE;;$
=@??E$
H0.#(22-+)0:L$
FDA<<$
?D@@=$
FAD>$
;>E>A$
*'40.(+$#(.9#$
(.5-+)(0.$
FAD<$
>;=;E$
80.0.9+2-0#(#$
J)4-/$)'40")$
Q)($54-B9-.+L$
8-"#2-#$
<AF=@$
><E=<$
B)2$C$@=AAA$
<@<?A$
B)2$C$<;AAA$
@A>;E$
FEFDD$
>E<??$
!-/")()(#$
=4%52$-&R$1$5&
=7".X2$#&
',.#"22$4-,8(&
+,#"-",.&
/0&
+1%23$&
5#?@AA6BC&
MNB6OBA&
5#?JCBA@B&
QNAEOBA&
5#D6@AAA6&
!SQANBCOB6&
5#CCBEDG&
!SMANBJOBA&
5#AAG??JJDC&
MNB6OBG&
5#D6@66G?&
QNBEOBA&
5#D6CEEE6&
!SMANBAOB6&
5#6B?DDB&
!SQANBJOBA&
5#6G6JGDA&
QN??OB6&
5#AAGE?C6??&
QNG@OBA&
6DDE@AE?&
AFAB&
AFBD&
BFEC&
BFEC&
BFD6&
BFDJ&
BFBC?U$RR$4-V&
BFB@EU$RR$4-V&
BF?J&
BFGE&
BFD6&
BFD6&
BF@G&
BF@G&
BFEG&
BFEJ&
AFA?&
BFEA&
AF6A&
AF66&
CFGBHIAB&
6F@CHIBE&
GFG@HIA6&
JFGDHIBE&
DF?JHIBE&
AFBCHIBG&
GF@AHI66&
JFDEHIA@&
6FDEHIAG&
@FGGHIAC&
?F6AHIBE&
AFGDHIBG&
CFA?HI6D&
JFADHI6A&
JFE@HIAB&
6FJCHIA6&
GF66HIAD&
DFDDHIA@&
CF@BHIA?&
AFADHIAG&
!
&
JA?JBJGD&
&
J6G@C@A@&
&
J6G@@JEB&
&
6DE6BJBB&
&
J6CBJBDE&
&
J6?JAC6J&
&
J6ACA?JB&
&
JAJ6CDCB&
&
JA?JBBCB&
&
!
!
!
!
!
!
!
!
!
!
!
*023#$2"#)$
L-"4$
B)2$C@=AAA$
D>@@$
O(.K(G()(#$
<>?;E$
F;@EE$
R'-9:")(+$
5-G-4$
<A?<$
;=<A?$
$
$
$
R9S-22"$
<<>@<$
EDF@D$
!
Table 1 Our discovery cohorts drawn from the more than 350,000
genotyped customers who reported via a web-based questionnaire
whether they had been diagnosed with certain kind of infectious
diseases. Sex and ancestry were determined based on genetic data; for
the GWAS, participants were of European ancestry.!
0$4")5,4%2&
+1%23$&
6FE?HIBJ&
JFBDHIBA&
6FG6HIBG&
6FBBHIBA&
JF6CHIB?&
6FCGHIBA&
CFC@HIBE&
JFC6HIB6&
AFJBHIBA&
@F?EHIB6&
6F@AHIBC&
GF@6HIBJ&
AF?AHIA?&
?F?DHIBC&
JFACHIB@&
?FC@HIBD&
AF??HIAB&
AFAJHIBC&
JFB?HIB6&
6FDEHIBA&
06&".&
4,7,5-&
BFDB&
&
BF?G&
&
BF?B&
&
BFGB&
&
BFEG&
&
BFBC&
&
BF6J&
&
BFB?&
&
BFA?&
&
BFEG&
&
&
BFDA&
&
BFDG&
&
AFBB&
&
BFD@&
&
BFD@&
&
BFDE&
&
BFDE&
!
!
!!
!
!
!
! !
!
!
!
!
!
30000000
!
!
!
31000000
!
SNP top hit
HLA top hit
additional SNP hits
additional HLA hits
32000000
Myringotomy
&
BFE@&
!
!!
Tonsillectomy
&
BFDE&
HLA−DPB1
HLA−DRB1
HLA−DQA1
HLA−DQB1
C2
C4A
C4B
HLA−C
HLA−B
MICA
MICB
TNF
!
Scarlet Fever
&
BFDE&
Independent GWAS hits
!
Mumps
*8)3-%-",.&
93%2"-(&
Table 2. Top association SNP and HLA signals.
!
!
!"#$MN$!%&$'()#$
8L4(.K0)0:L$
+,#"-"1$&'Q&
C
*"#-#$
*0.)402$
*'(+,-.$/01$
W38)#&
+.$38,."%&
ch ch
ild ild
ho ho
od od
_ _ p p
C
to to
s s
hi hi C C ear ear lan lan
pn pn po po ca ca
ns ns
ck ck ol ol _i _i ta ta
s s r r
en en d_ d_ nfe nfe r_ r_ m m eum eum itiv itiv let_ let_ sh sh ille ille
_P _P so so ct ct wa wa um um o o e_ e_ fe fe ing ing cto cto
ox ox res res ion ion rts rts ps ps nia nia TB TB ve ve les les my my
r r
Phenotypic Data
!
+2%.-%5&T%5-#&
*!&
=>+&
KLM&
=>+&
KLM&
=>+&
KLM&
=>+&
KLM&
=>+&
KLM&
=>+&
KLM&
=>+&
KLM&
=>+&
KLM&
=>+&
KLM&
=>+&
KLM&
HLA−G
HLA−A
We conducted genome-wide association study (GWAs) on 23 infectious
diseases. All participants were drawn from the customer base of
23andMe, Inc., a personal genetics company. All participants were of
primarily European ancestry, and no pair was more closely related than
at the level of first cousins. To explore the relationship between individual
HLA alleles and susceptibility to infectious diseases we used HIBAG1, a
statistical method combining a large database of individuals with known
HLA alleles and SNP variation within the MHC region, to impute HLA
alleles at key class I and class II loci over all 23andMe customers of
European ancestry. We identified the primary association signal (lowest
Pvalue) for each disease and performed iterative conditional regression
to identify other independent signals in MHC region. All analysis
controlled for sex and five principal components of genetic ancestry. Pvalues were calculated using likelihood ratio test.!
:7"2P7,,P&$%5&
".R$4-",.&
'()$&
Childhood ear infection
33000000
Figure 2. GWAS signals outside MHC region.!
Conclusion
•  The conciliation of current high density SNP analysis and association
test with imputed HLA alleles confirms that the predominant signals
in many infectious diseases map to specific HLA regions and certain
HLA peptides play important roles in modulating infectious diseases. !
•  There seem to be multiple independent HLA genes involves in the
susceptibility to tonsillectomy, shingles, scarlet fever, positive TB test,
pneumonia, plantar warts, cold sores and chicken pox by conditional
analysis. !
•  The top SNP associations seem all stronger (lower Pvalue) than HLA
allele associations except scarlet fever. The top associated SNP
might be a proxy for multiple functional HLA alleles which contribute
combinedly in viral/bacterial peptide interaction.!
•  Shingles and chicken pox are related infectious diseases, the result
from the current study, strongly implicated a shared association with
HLA-A*02:01 allele.!
•  A more detail analysis of specific amino acids changes in HLA
peptide of the association HLA alleles2 will further help to define the
host genetic effects on the outcome of the corresponding infectious
diseases.!
Acknowledgments
We thank the customers of 23andMe for participating in this research
and the employees of 23andMe for their contributions to this work.!
Chromosome Positions
Genotyping
Participants were genotyped for 586,916 to 1,008,949 on one of three
illumine –based beadchips. An additional 7,356,559 imputed SNPs were
included in the analysis.!
Figure 1. All independent association SNP and HLA signals.!
We also detected signals outside MHC region for Childhood ear
infection, mumps, scarlet fever, tonsillectomy and myringotomy (Figure
2). Variants in FUT2 are highly associated with susceptibility to childhood
ear infection and mumps. TBX1 locus is highly associated with
susceptibility to childhood ear infection, myringotomy and tonsillectomy. !
The shared effects highlighted potentially shared functional pathways.!
References
1. Zheng X, Shen J, Cox C, Wakefield J, Ehm M, Nelson M, Weir BS.
HIBAG – HLA Genotype Imputation with Attribute Bagging.
Pharmacogenomics Journal (2013).!
2. International HIV Controllers Study, Pereyra F and Zhao M. et al.
The major genetic determinants of HIV-1 control affect HLA class I !
peptide presentation. Science. 2010 Dec 10;330(6010):1551-7.!