Advertisement

BMC Medical Genomics

, 12:130 | Cite as

Host genetic variability and pneumococcal disease: a systematic review and meta-analysis

  • Anne T. Kloek
  • Matthijs C. Brouwer
  • Diederik van de BeekEmail author
Open Access
Research article
Part of the following topical collections:
  1. Genomic epidemiology

Abstract

Background

Pneumonia, sepsis, meningitis, and empyema due to Streptococcus pneumoniae is a major cause of morbidity and mortality. We provide a systemic overview of genetic variants associated with susceptibility, phenotype and outcome of community acquired pneumococcal pneumonia (CAP) and invasive pneumococcal disease (IPD).

Methods

We searched PubMed for studies on the influence of host genetics on susceptibility, phenotype, and outcome of CAP and IPD between Jan 1, 1983 and Jul 4, 2018. We listed methodological characteristics and when genetic data was available we calculated effect sizes. We used fixed or random effect models to calculate pooled effect sizes in the meta-analysis.

Results

We identified 1219 studies of which 60 studies involving 15,358 patients were included. Twenty-five studies (42%) focused on susceptibility, 8 (13%) on outcome, 1 (2%) on disease phenotype, and 26 (43%) on multiple categories. We identified five studies with a hypothesis free approach of which one resulted in one genome wide significant association in a gene coding for lincRNA with pneumococcal disease susceptibility. We performed 17 meta-analyses of which two susceptibility polymorphisms had a significant overall effect size: variant alleles of MBL2 (odds ratio [OR] 1·67, 95% confidence interval [CI] 1·04–2·69) and a variant in CD14 (OR 1·77, 95% CI 1·18–2·66) and none of the outcome polymorphisms.

Conclusions

Studies have identified several host genetics factors influencing risk of pneumococcal disease, but many result in non-reproducible findings due to methodological limitations. Uniform case definitions and pooling of data is necessary to obtain more robust findings.

Keywords

Host genetic variability Pneumococcal disease Systematic review Meta-analysis 

Abbreviations

AA

African Americans

ADRB2

β2-adrenoceptor

CAP

Community acquired pneumococcal pneumonia

CPB2

Carboxypeptidase B2

CRP

C-reactive protein

EA

European Americans

Fc

Fragment crystallizable

FVL

Factor V Leiden

GLCCI1

Glucocorticoid-induced transcript 1 gene

Ig

Immunoglobulin

IKBKG

Inhibitor of nuclear factor kappa-B kinase subunit gamma

IL

Interleukin

IPD

Invasive pneumococcal disease

lincRNA

Long intergenic non-coding RNA

MAF

Minor allele frequency

MASP

MBL-associated serine protease

MBL

Mannose-binding lectin

MIF

Macrophage migrating inhibitory factor

NFκB

Nuclear factor kappa-light-chain-enhancer of activated B cells

NLR

Nod-like receptor

NOS2

Nitric oxide synthase 2

PAI-1

Plasminogen activator inhibitor 1

PCR

Polymerase chain reaction

PTP

Protein thyrosine phosphatase

SFTPA

Surfactant protein A

SFTPD

Surfactant protein D

SNP

Single nucleotide polymorphism

TAFI

Thrombin-activatable fibrinolysis inhibitor

TIRAP

Toll interleukin-1 receptor domain-containing adaptor protein

TLR

Toll-like receptor

TNF

Tumor necrosis factor

Background

Pneumococcal infection is a major cause of morbidity and mortality worldwide [1]. Invasive pneumococcal disease (IPD) is an infection confirmed by the isolation of Streptococcus pneumoniae from a normally sterile site, while non-invasive pneumococcal disease includes sinusitis, mastoiditis, acute otitis media, and community-acquired pneumonia (CAP). Streptococcus pneumoniae has been identified as the most common cause of CAP in adults [2, 3, 4]. In 2015, an estimated 515.000 deaths (range 302.000–609.000) were attributed to pneumococcal infection among children less than 5 years of age globally [5]. The incidence of IPD is strongly age-related, with the highest incidence in younger children and the elderly with incidence ranging from 11 to 27 per 100,000 in Europe [6, 7, 8]. Mortality rates for IPD vary from 12 to 22% in adults in the western world and are substantially higher in low income countries [7, 8, 9, 10, 11].

Pneumonia with empyema and/or bacteraemia, meningitis, and bacteraemia are the commonest manifestations of IPD. [12] Identified risk factors for IPD include splenectomy, cancer, and diabetes mellitus, but in a substantial proportion of patients no risk factor can be identified [7]. Extreme phenotype studies in patients with recurrent or familial IPD first identified genetic risk factors to increase susceptibility [13]. Most of the identified genetic variation was found in genes controlling the host response to microbes [14]. Subsequently several case–control and cohort studies described genetic variation to increase susceptibility and to predict unfavourable outcome of pneumococcal disease and disease phenotype [6, 9, 15].

In the past 20 years several genetic association studies investigated host genetics in relation to susceptibility and outcome of pneumococcal disease, sometimes showing conflicting results. Here we systematically review these studies, perform a meta-analysis and discuss the potential of these findings for understanding the pathophysiological mechanisms of pneumococcal disease.

Methods

Systematic review

We performed a systematic review and meta-analysis with the objective to summarize host genetic variation associated with susceptibility, phenotype or outcome of patients with IPD and CAP. The following search terms were used in PubMed: ((Streptococcus pneumoniae) OR (S. pneumoniae) OR pneumococcal OR pneumococcus) AND (polymorphisms OR polymorphism OR (genetic variant) OR (genetic variants) OR (genetic association study) OR (single nucleotide polymorphism) OR (single nucleotide polymorphisms) OR SNP OR SNPs OR genotype OR genotypes) without language restrictions and with search date cut offs between Jan 1, 1983 and Jul 4, 2018. We identified additional publications by checking the references in those published studies and via communicating with experts in the field. Extreme phenotype, review studies, and studies with specific patients groups like immunocompromised patients were excluded. Studies were eligible for inclusion if the population of interest was reported with at least one of the outcome measures.

Meta-analysis and statistical analyses

Each study was scored for methodological quality, such as study design, definition of the investigated condition, ethnicity of included patients, sample size, selection of the control group, quality control of genotyping, statistical methods and correction for multiple testing. We performed meta-analyses for multiple studies that assessed a single genetic polymorphism (or a combination of polymorphisms) of which genotype data was available in the manuscript. Different nomenclatures of genetic variants included in the review can be found in Additional file 1: Table S1. Review Manager 5.3 was used to generate Forest plots and calculate overall effect sizes with a fixed effects model or random effects model if the results between studies were too heterogeneous (Q test for homogeneity p < 0.05) [16]. The funder of the study had no role in study design, data collection, data analysis, data interpretation, or writing of the report. The corresponding author had full access to all the data in the study and had final responsibility for the decision to submit for publication.

Results

Systematic review

The date of search was 4 July 2018 and yielded 1219 articles (Fig. 1 - flow diagram) of which 60 articles were eventually included in the review [17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76]. Studies were published from 2000 to 2018 and contained 16,034 patients included in 27 different cohorts from 15 countries. There was a substantial overlap of cohorts and patients between the published articles. Of all studies, 24 (40%) analysed the influence of genetic variation on susceptibility to pneumococcal disease, 8 (13%) on outcome, 2 (3%) on disease phenotype, and 26 (43%) studies assessed multiple categories of which 24 (40%) on susceptibility and outcome (Tables 1 and 2). Eight studies (13%) focused on patients with pneumococcal CAP, 49 studies (82%) on patients with IPD and 3 studies (5%) on IPD and pneumococcal CAP.
Fig. 1

Flow diagram for study selection

Table 1

Genetic-association studies on susceptibility to pneumococcal disease

Name, year, reference

Candi-date gene

Genetic variants*

Country of origin (ethnicity)

Patient groups

Patient -selection

N

Controls - selection

N

Results – Gene, genetic variation, risk allele/genotype: p-value, OR (95% CI) †

Pathogen recognition receptor signalling pathways

Khor, 2007, [29]

Cohort 1

TIRAP

31 variants

United Kingdom

(white)

All ages with IPD

Blood, CSF, or joint fluid culture

191

Blood donors and cord blood samples

741

rs8177374 heterozygosity: p = 0.013, OR 0.65 (0.44–0.97)

Khor, 2007

Cohort 2

TIRAP

31 variants

United Kingdom

(unspecified)

Pleural empyema

Empyema culture

36

Healthy adult blood donors

361

rs8177374 heterozygosity: p = 0.08, OR 0.74 (0.32–1.68)

Khor, 2007

Cohort 3

TIRAP

31 variants

Kenya

(African)

Children with bacteraemia

Blood culture

164

Community-based

423

rs8177374 heterozygosity: p = 0.024, OR 0.30 (0.06–0.99)

Moens, 2007, [30]

TLR2 TLR4

rs5743703

rs5743704

rs5743708 rs4986790

Belgium

(white)

All ages with IPD

Blood, CSF, or joint fluid culture

99

Family of hospital personnel, university employees

178

NS

Yuan, 2008, [33]

TLR2 TLR4

CD14

rs5743708

rs4986790

rs4986791

rs2569190

Australia

(unspecified)

Children with IPD

Blood culture

85

Healthy blood donors

409

- TLR4 rs4986790/rs4986791 AG + GG/CT + TT genotypes: P < 0.05, OR 0.3 (0.1–1)

- CD14 rs2569190-CC genotype: P < 0.05, OR 1.7 (1–2.8)

Sanders, 2011, [43]

TLR9

rs5743836

rs352140

Netherlands

(white)

Children and adolescents with BM

CSF culture

83

Healthy white adults without a known history of BM

392

NS

Van Well, 2013, [57]

TLR2

TLR4

NOD1

NOD2 CASP1

rs5743708

rs4986790

rs6958571

rs2066844

rs2066845

rs2066847

rs2282659

Netherlands

(white)

Children with BM

CSF culture

82

Ethnically matched healthy controls

1141

NS

Tellería-Orriols, 2013, [59]

TLR2

TLR 4 CD14

rs5743708 rs4986790rs2569190

Spain (white)

Children with IPD

Culture of sterile site, PCR or antigen

114

Healthy White children

66

- TLR2 rs5743708-GA + AA genotypes: p < 0.0001, OR 4.26 (2.19–8.3)

- CD14 rs2569190-CC: p = 0.0167, OR 1.93 (0.95–3.91)

Ellis, 2015, [63]

IRAK4

MYD88 IKBKG

233 variants

United Kingdom

(white)

All ages with IPD

Culture of sterile site

164

Geographically-matched population-based controls

164

IRAK4 rs4251513 variant allele: p = 9.96 × 10− 3, OR 1.50 (1.10–2.04)

Carrasco-Colom, 2015, [65]

IRAK4 IRAK1 IRAKM

MYD88

10 variants

Spain

(mixed, 92% white)

Children with IPD and SIRS

Culture or PCR of sterile site

60

Patients with no previous immunodeficiency or IPD, nor concomitant infectious pathology

120

P-value in article adjusted by false discovery rate: not repoducible by re-calculation

- IRAK1 rs1059701-CC

- IRAK4 rs4251513-CC

- IRAK4 rs1461567-T

- MYD88 rs6853-AA

Gowin, 2017, [74]

TLR2 TLR4 TLR9

rs5743708

rs4696480

rs4986790

rs352140

rs5743836

Poland

(White)

Children with BM

CSF culture or PCR

14

Family members

49

NS

Gowin, 2018, [76]

TIRAP

TLR2 TLR4 TLR9

rs8177374

rs4696480

rs5743708

rs4986790 rs5743836 rs352140

Poland

(white)

Children with bacterial meningitis

CSF culture or PCR

14

Family members

49

- TIRAP rs8177374 variant allele carriers: p = 0.0508, OR 4.5 (0.96–21.12)

- TIRAP rs8177374 and MBL2 rs1800451 variant alleles cumulative effect: p = 0.035, OR = 4.9 (1.17–20.48)

Complement system

Roy, 2002, [19]

MBL2

rs5030737

rs1800450

rs1800451

rs7096206

United Kingdom (white)

All ages with IPD

Sterile body site

337

Donors, neonates

1032

MBL2 O/O genotype: p = 0.002, OR 2.59 (1.39–4.83)

Kronborg, 2002 [18]

MBL2

rs5030737

rs1800450

rs1800451

rs7096206

Denmark

(mixed, 97.9% white)

Adults with IPD

Blood culture

140

Blood donors, laboratory personnel

250

NS

Moens, 2006, [27]

MBL2

rs5030737

rs1800450

rs1800451

rs7096206

Belgium

(white)

All ages with IPD

Blood, CSF, or joint fluid culture

63

Sex-matched hospital employees, urology, and internal medicine outpatients

162

NS

Endeman, 2008, [35]

MBL2

rs5030737

rs1800450

rs1800451

rs7096206

Netherlands

(not specified)

Adults with CAP

Blood / sputum culture

60

Blood bank donors

223

NS

Garcia-Laorden, 2008, [49]

MBL

MASP-2

rs5030737

rs1800450

rs1800451

rs72550870

Spain

(white)

Adults with CAP

Clinical symptoms and radiographic findings

195

Healthy control subjects, a control group of patients without relevant infectious diseases

1447, 519

NS

Garcia-Laorden, 2011, [44]

SFTPA1

SFTPA2

SFTPD

rs1059047

rs1136450 rs4253527 rs1059046 rs17886395 rs4253527

rs721917

Spain

(white)

Adults with CAP

Clinical symptoms and radiographic findings and blood culture

326

Blood and bone marrow donors as well as hospital staff and patients without signs of relevant infectious diseases

1538

Associations below p < 0.05:

- 5 haplotypes of SFTPA1, SFTPA2 and SFTPD

Garcia-Laorden, 2012, [49]

Cohort 1

MBL2

rs5030737 rs1800450 rs1800451

Spain

(white)

Adults with CAP

Clinical symptoms and radiographic findings and blood culture

340

Blood and bone marrow donors, hospital staff and patients without signs of relevant infectious diseases

1736

NS

Garcia-Laorden, 2012

Cohort 2

MBL2

rs5030737 rs1800450 rs1800451

Spain

(unspecified)

Adults with CAP

Blood, pleural fluid, sputum (+ bacterial recount or positive urinary antigen) culture

84

Healthy controls

91

NS

Brouwer, 2013, [58]

MBL2

rs5030737 rs1800450 rs1800451 rs7096206

Netherlands

(white)

Adults with BM

CSF culture

299

Partners, non-related proxies

216

MBL2 O/O genotype: p  =  0.017, OR 8.21 (1.05–64.1)

Adriani, 2013, [56]

C3 C5 C6 C7 C8B C9 CFH

17 variants

Netherlands (mixed, 94% white)

Adults with BM

CSF culture

299

Partners, non-related proxies

216

NS after correction.

C7 rs13157656 dominant model: p = 0.04 OR 1.46 (1.02–2.09)

C3 rs1047286 recessive model p = 0.03 OR 3.14 (1.08–9.19)

Lundbo, 2014, [62]

MBL2

rs5030737 rs1800450 rs1800451 rs7096206

Scandinavia, Germany

(unspecified)

Children with IPD

CSF, blood or other sterile site culture

1279

Age- and sex-matched

1263

NS

Mills, 2015, [64]

MBL2

rs5030737 rs1800450 rs1800451 rs7096206

United Kingdom

(unspecified)

Sepsis in adults with CAP

Not specified

95

Individuals attending general practice surgeries for reasons other than infection

477

NS

Gowin, 2018, [76]

MBL2

CFH

CFHR3

rs5030737 rs1800450 rs1800451

rs1065489

rs3753396

Poland

(white)

Children with BM

CSF Culture or PCR

14

Family members

49

TIRAP rs8177374 and MBL2 rs1800451 cumulative effect: p = 0.035, OR = 4.9 (1.17–20.48)

Fcγ receptors

Yee, 2000, [17]

FCGR2A

rs1801274

USA

(mixed)

B-CAP (age not specified)

Blood or sputum culture

42

Randomly selected hospital patients

136

R131/R131 genotype: p < 0.05, OR 2.40 (1.18–4.87)

Yuan, 2003, [22]

FCGR2A

rs1801274

Australia (unspecified)

Children with sepsis

Blood culture, Ag in blood donors

63, 34

Children from vaccination programme/ Healthy blood donors

20, 57

R131/R131 genotype: P = 0.01, OR 2.81 (1.25–6.32)

Chapman, 2006, [25]

PTPN22

rs2476601

UK

(white)

All ages with IPD

Culture of sterile body site

286

Ethnically matched

803

T allele: P = 0.004, OR = 1.56 (1.15–2.11)

Moens, 2006, [24]

FCGR2A

rs1801274

Belgium

(white)

All ages with IPD

Blood, cerebrospinal fluid, or joint fluid culture

55

Sex-matched hospital employees, urology, and internal medicine outpatients

100

NS

Yuan, 2008, [33]

FCGR2A

rs1801274

Australia (unspecified)

Children with IPD

Blood culture

85

Healthy blood donors

409

R131/R131 genotype: P < 0.001, OR 2.46 (1.49–4.04)

Endeman, 2009, [37]

FCGR2A

rs1801274

Netherlands (unspecified)

Adults with CAP

Blood / sputum culture, urine antigen

60

Healthy unrelated Whites from the same geographical area

314

NS

Solé-Violán, 2011, [45]

FCGR2A FCGR3A

rs1801274

rs396991

Spain

(white)

Adults with CAP and B-CAP

Blood culture, urine antigen

CAP = 319, B-CAP = 85

Unrelated healthy volunteers and patients without a previous history of relevant infectious diseases

1224

B-CAP FCGR2A– H131/H131 genotype: p = 0.01, OR 1.81 (1.09–2.43)

Bouglé, 2012, [52]

FCGR2A

rs1801274

France

(white)

Adults with IPD

Culture of sterile site

243

ICU patients without infection

2789

NS

NFκβ signalling pathway

Chapman, 2007, [32]

NFKBIA NFKBIB NFKBIE

43 variants (very rare excluded)

UK

(white)

All ages with IPD

Blood, CSF, or joint fluid culture

288

Blood donors and cord blood samples

770

NFKBIA rs3138053 variant allele carriers: p = 0.0003, OR 0.60; (0.45–0.79)

NFKBIA rs2233406 variant allele carriers: p = 0.00001, OR 0.55 (0.42–0.72)NFKBIE rs529948 variant allele carriers, p = 0.001, OR 0.59 (0.43–0.83)

Chapman, 2010, [38]

Cohort 1

NFKBIZ

15 variants, (very rare excluded)

UK

(white)

All ages with IPD

Culture from sterile site

275

Healthy adult blood donors, cord blood samples

163, 570

3 × 2 Chi-squared comparisons of genotypes, p-values below 0.05:

rs600718: p = 0.01, rs616597: p = 0.001, rs685666: 0.036, rs6441627: 0.011, rs587555: p = 0.05, rs677011: 0.042, rs601225: p = 0.049

Chapman, 2010

Cohort 2

NFKBIZ

15 variants, (very rare excluded)

Kenya

(African)

Children with IPD

Blood culture

173

Age and sex matched community-based

550

3 × 2 Chi-squared comparisons of genotypes: p-values below 0.05:

rs600718: p = 0.022

Chapman, 2010, [40]

Cohort 1

NFKBIL2

9 variants

UK

(white)

All ages with IPD

Culture from sterile site

275

Healthy adult blood donors, cord blood samples

163, 570

Both cohorts:

rs760477 heterozygosity: p = 0.0006, OR = 0.67 (0.53–0.84)rs4925858 heterozygosity: p = 0.003, OR = 0.70 (0.55–0.88)

Chapman, 2010

Cohort 2

NFKBIL2

9 variants

Kenya

(African)

Children with IPD

Blood culture

173

Age and sex matched community-based

550

Sangil 2018, [75]

NFKBIA NFKBIE NFKBIL2 NFKBIZ

10 variants

Spain

(white)

Adults with IPD

Not specified

144

Ethnically matched

280

NFKBIA rs1050851-T: p = 0.04

NFKBIE rs2282151-C: p = 0.02NFKBIZ-CC rs645781: p = 0.02

Cytokines

Schaaf, 2003, [21]

IL10

TNF

LTA

rs1800896 rs1800629 rs909253

Germany (white)

CAP and IPD

(age not specified)

CSF, blood, pleural fluid, sputum culture

69

Unrelated age and sex-matched orthopaedic patients

50

NS

Schaaf, 2005, [23]

IL6

rs1800795

Germany (white)

CAP and IPD

(age not specified)

CSF, blood, pleural fluid, sputum culture

100

Age matched

50

NS

Carrol, 2011, [42]

IL-1Ra

rs4251961

Malawi

(African)

Children with IPD

Blood, sputum, CSF culture or Ag test or PCR

299

Healthy controls

933

NS

Martin- Loeches, 2012, [48]

IL6

rs1800795

Spain

(white)

Adults with CAP

Blood culture, urine antigen

306

953 white Spanish unrelated healthy volunteers, 434 patients without a previous historyof relevant infectious diseases

1387

NS

Savva, 2016, [68]

MIF

rs5844572 rs755622

Netherlands

(white)

Adults with

BM

CSF culture

405

Partners, non-related proxies

329

NS

Sangil, 2018, [75]

IL10

IL12B

IL1A

IL1B ILR1 IL4

33 variants

Spain

(white)

Adults with IPD

Not specified

144

Ethnically matched

280

IL1R1 rs3917254-CC: p = 0.04

Coagulation and fibrinolysis

Benfield, 2010, [39]

FVL

rs6025

Denmark

(unspecified)

Adults with IPD

Culture of CSF, blood or other sterile site

163

Age matched adults without infectious disease hospitalization

8147

NS

Mook, 2015, [66]

CPB2 (TAFI)

rs1926447 rs3742264

Netherlands

(white)

Adults with BM

CSF culture

716

Partners, non-related proxies

Not shown

NS

Other

Roy, 2002, [20]

CRP

rs3138528

United Kingdom (white)

All ages with IPD

Blood, CSF, or joint fluid culture

205

Randomly selected local blood donors and transplant donors

345

Common allele: P = 0.001; OR 1.52 (1.18–1.96)

Chapman, 2007, [31]

FCN2

rs3124952 rs3124953 rs17514136 rs17549193 rs7851696

United Kingdom

(white)

All ages with IPD

Blood, CSF, or joint fluid culture

290

Blood donors and cord blood samples

720

NS

Payton, 2009, [36]

NOS2A

9 variants

Malawi

(African)

Children with IPD

Culture, PCR, antigen tests

229

Age matched

931

NS

Adriani, 2012, [51]

ADRB2

rs1042713 rs1042714

Netherlands

(mixed, 94% white)

Adults with BM

CSF culture

396

Partners, non-related proxies

376

rs1042714 Gln/Glu genotype: p  =  0.007, OR 1.52 (1.12–2.07)

Brouwer, 2012, [54]

GLCCI1

rs37972

Netherlands

(white)

Adults with BM

CSF culture

699

Partners, non-related proxies

490

NS

Studies with genes in mixed categories

Lingappa, 2011, [46]

34 genes

326 variants

USA (European Americans (EA) and African Americans (AA))

Children with IPD

Culture of sterile site

EA = 182

AA = 53

Bloodspot collection from new-borns, race/ethnicity and date of birth matched

361, 113

Associations below p < 0.05, none of the variants in both EA and AA):

- In AA: 11 variants in 6 genes (CD46, SFTPB, SFTPD, IL1B, ILIR1, PTAFR)

- in EA: 17 variants in 9 genes (CD46, SFTPA1, SFTPD, IL1B, ILIR1, IL4, IL10, IL12B, FAS)

Lundbo, 2015, [67]

NFKBIE

NFKBIA

NFKBIL2

NFKBIZ

TIRAP

PTPN22

rs529948 rs3138053 rs2233406 rs760477 rs616597

rs8177374

rs2476601

Scandinavia, Germany

(unspecified)

Children with BM/ bacteraemia

CSF or blood culture

372, 907

Age and sex matched

1273

Pneumococcal meningitis:

NFKBIE rs529948 variant allele carriers, p = 0.0001, OR 1.68 (1.20–2.36) Combined patient groups:

NFKBIE rs529948 variant allele carriers, p = 0.01, OR 1.24 (1.03–1.49)

Other: NS

Hypothesis free studies

Ellis, 2015, [63]

Sequencing of IRAK4

MYD88 IKBKG

233 variants

United Kingdom

(white)

All ages with IPD

Culture of sterile site

164

Geographically-matched population-based

164

IRAK4 rs4251513 variant allele: p = 9.96 × 10−3, OR = 1.50 (1.10–2.04)

Ferwerda, 2016, [72]

Sequencing of

46 genes

1854 variants

Netherlands

(white)

Adults with BM

CSF culture

435

Partners, non-related proxies

416

CARD8 rs2008521-T allele: p = 8.2 × 10−4, OR 1.82 (1.28–2.75) CXCL1 rs56078309-A allele: p = 8.2 × 10− 4, OR 1.96 (1.34–2.87)

Kenyan Bacterae-mia Study Group, 2016, [70]

GWAS

787,861 variants,

10 million variants after imputation

Kenya

(African)

Children with bacteraemia

Blood culture

429

113

Sex, ethnic group, and geographic area matched controls

2677

1136

17 variants above genome-wide significance (p < 5 × 10−8). Strongest association in discovery cohort: (minor allele = risk allele)

LincRNA rs14081715- additive model: p imputed = 7.25 × 10−9, OR = 2.74

Replication cohort:

LincRNA rs14081715- additive model: p = 0.001, OR 2.72

Kloek, 2016, [71]

Exome array analysis

102,097 variants

Netherlands

(white)

Adults with BM

CSF culture or PCR

469

Population-based controls

2072

COL11A1 rs139064549-G allele: p = 1.51 × 10−6, OR 3.21 (2.05–5.02) EXOC6B rs9309464-G allele: p = 6.01 × 10−5, OR 0.66 (0.54–0.81)

Abbreviations: Ag agglutination, BM bacterial meningitis, B-CAP bacterial-CAP, CAP community acquired pneumoniae, CI confidence interval, CSF cerebrospinal fluid, GWAS genome wide association study, IPD invasive pneumococcal disease, NS not significant, OR odds ratio, PCR polymerase chain reaction, PM pneumococcal meningitis

*Genetic variants: Synonyms of genetic variants can be found in Supplementary Table 1. Results: None of the p-values are corrected for multiple testing

Table 2

Genetic-association studies on outcome and phenotype of pneumococcal disease

Name, year,

PMID

Candidate gene

Genetic variants*

Country of origin (ethnicity)

Patient groups

N

Patients- Selection criteria

Outcome measures -(% mortality, adverse events)

Results – Gene, genetic variation, risk allele/genotype: p-value, OR (95% CI)

Pathogen recognition receptor signalling pathways

Van Well, 2012, [53]

TLR2 TLR4 TLR9 NOD1 NOD2 CASP1 TRAIL

11 variants

Netherlands

(white)

Children with BM

66

CSF culture

Hearing loss (21%)

- TLR9 rs5743836 TC and CC genotypes: p  =  0.023, OR 2.5 (1.1–5.4) and p  =  0.017, OR 5.0, (1.4–17.4)Combined carriership:

- TLR2 rs5743708 and TLR4 rs4986790 AG genotype: p   =  0.03, OR 13.9 (1.3–147)- TLR4 rs4986790 and TLR9 rs5743836 mutant alleles: p  =  0.003, OR 6.0 (1.7–21.3)

Garnacho-Montero, 2012, [50]

TLR2 TLR4

rs5743708

rs4986790

rs4986791

Spain

(white)

Adults with sepsis

117

Sterile site and BAL/tracheal aspirate culture

Septic shock (34%)

In-hospital mortality (18.8%)

90 day mortality (21.4%)

NS

Carrasco-Colom, 2015, [65]

IRAK4

IRAK1 IRAKM MYD88

10 variants

Spain

(mixed, 92% white)

Children with IPD and SIRS

60

Sterile site culture or PCR

Pleuro-pneumonia (7%)

Sequelae (33%)

Mortality (3%)

Serotypes

- Pleuropneumonia: IRAKM rs1624395-G and rs1370128-C; p = 0.0147, OR 1.83 (1.23–2.74) and p = 0.0055, OR 2.06 (1.37–3.11)

- Sequelae: IRAK4 rs4251513-nonGG: p = 0.0010,

OR 7.07 (2.64–18.87)- Death: MYD88 rs6853-nonAA and rs6853-G: p = 0.0054, OR 16.09 (3.34–77.57) and p = 0.0064, OR 8.39 (2.47–28.46)

- Serotypes: NS

Complement system

Kronborg, 2002, [18]

MBL2

rs7096206 rs5030737 rs1800450 rs1800451

Denmark

(mixed, 97.9% white)

Adults with IPD

140

Blood culture

Outcome (not specified, mortality of 17%)

NS

Perez, 2006, [26]

MBL-2

rs5030737 rs1800450 rs1800451

Spain

(unspecified)

Adults with CAP

97

Blood or pleural fluid culture/ sputum culture + positive Ag test or quantitative bacterial count

Bacteraemia (53%)

Risk class of mortality (Fine scale) I (15%), II (11%), III (17%), IV (42%), V (15%)

- Bacteraemia: MBL2 AA genotype: p = 0.02, OR 2.74 (1.01–7.52)- Risk class mortality: NS

Endeman, 2008, [35]

MBL2

rs5030737 rs1800450 rs1800451 rs7096206

Netherlands

(unspecified)

Adults with CAP

60

Blood or sputum culture

Outcome - ICU admission (11%), length of hospital stay (median 11 (range 2–153)

NS

Garcia-Laorden, 2008, [34]

MBL

MASP-2

rs5030737 rs1800450 rs1800451 rs7096206

rs72550870

Spain

(white)

Adults with CAP

195

Clinical symptoms and radiographic findings

Severe sepsis (16%), septic shock (14%) ICU admission (22%), MODS (10%), high pneumonia severity index (59%), bacteraemia (8%), ARF (70.9%), ARDS (5%), 90 day mortality (9%)

NS

Woehrl, 2011, [47]

C3 C5 C6 C7 C8B C9 CFH

17 variants

Netherlands

(white)

Adults with BM

217

CSF culture

Outcome - unfavourable (24%) vs favourable (76%, GOS 5)

C5 rs17611-GG genotype: p = 0.002, OR 2.25 (1.33–3.81)

Garcia-Laorden, 2012, [49]

MBL2

rs5030737 rs1800450 rs1800451 rs7096206

Spain

(white)

Adults with CAP

346

Clinical symptoms and radiographic findings and blood culture

Severity sepsis, ICU admission (38%), acute renal failure (33%), MODS (21%), high pneumonia severity index (56%), bacteraemia (28%), ARF (72%), ARDS (8%), 90 day mortality (7%)

NS

Garnacho-Montero, 2012, [50]

MBL-2

rs1800450 rs1800451 rs5030737

Spain

(white)

Adults with sepsis

117

Culture of sterile site

Septic shock (34%)

In-hospital mortality (19%)

90 day mortality (21%)

MBL2 AO/OO variants:

- Septic shock: aHR 15.3 (3.5–36.5)- In hospital mortality: aHR 3.2 (1.01–9.8)

- 90 day mortality: aHR 2.2, (1.1–8.1)

Brouwer, 2013, [58]

MBL2

rs5030737 rs1800450 rs1800451 rs7096206

Netherlands

(white)

Adults with BM

299

CSF culture

Septic shock, systemic complications

Mortality (8%), - unfavourable outcome: GOS 1–4 (28%)

Serotypes

NS

Lundbo, 2014, [62]

MBL2

rs5030737 rs1800450 rs1800451 rs7096206

Scandinavia, Germany

(unspecified)

Children with IPD

1279

CSF, blood or other sterile site culture

Mortality (2%) and serotypes

NS

Muñoz-Almagro, 2014, [61]

MBL2

rs5030737 rs1800450 rs1800451 rs7096206

rs11003125

rs7095891

Spain

(mixed)

All ages with IPD

147

CSF, blood, sterile body site culture or PCR

Serotypes, children < 18 years (69%) vs adults (31%)

- Children < 2 years vs other MBL2 O/O and XA/O: p = 0.031- Children < 2 years vs other (opportunistic or low-attack-rate serotypes only) MBL2 O/O and XA/O: p = 0.02

Mills, 2015, [64]

MBL2

rs5030737 rs1800450 rs1800451 rs7096206

United Kingdom

(unspecified)

Sepsis in adults with CAP

245

Not specified

28-day mortality from CAP sepsis (19%)

NS

Kasan-moentalib, 2017, [73]

MASP-2

rs2273346 rs12711521 rs12142107 rs139962539

Netherlands (white)

Adults with BM

397

CSF Culture

Unfavourable outcome: GOS scale 1–4 (32%)

NS

Fcγ receptors

Solé-Violán, 2011, [45]

FCGR2A FCGR3A

rs1801274

rs396991

Spain

(white)

Adults with CAP and B-CAP

CAP:319, B-CAP:85

Blood culture, urine antigen

Acute renal failure (32%), ARDS (8%), severe sepsis (41%), 28 (4%) and 90 day (6%) mortality

Bacteraemic vs non-bacteraemic CAP: FCGR2A-H/H: p = 0.00016, OR 2.9 (1.58–5.3)

B-CAP and CAP:

- Acute renal failure FCGR2A-H/H: p = 0.004, OR 2.32

- Acute respiratory stress syndrome FCGR2A-H/H: p = 0.047, OR 2.17- Severe sepsis FCGR2A-H/H: p = 0.037. OR 1.8

Garnacho-Montero J, 2012, [50]

FCGR2A

rs1801274

Spain

(white)

Adults with sepsis

117

Culture of sterile site

Septic shock (34%)

In-hospital mortality (19%)

90 day mortality (21%)

NS

Bouglé, 2012, [52]

FCGR2A

rs1801274

France

(white)

Adults with IPD

243

Culture of sterile site

Hospital mortality (31%)

Hospital mortality FCGR2A-H/H: p = 0.004, OR 0.251 (0.098–0.645)

NFκβ signalling pathway

Chapman, 2010, [38]

Cohort 1

NFKBIZ

15 variants, (very rare excluded)

UK

(white)

All ages with IPD

275

Culture from sterile site

Outcome (not specified)

NS

Chapman, 2010

Cohort 2

NFKBIZ

15 variants, (very rare excluded)

Kenya

(African)

Children with IPD

173

Blood culture

Outcome (not specified)

NS

Chapman, 2010, [40]

Cohort 1

NFKBIL2

9 variants

UK

(white)

All ages with IPD

275

Culture from sterile site

Mortality (10%)

NS

Chapman, 2010

Cohort 2

NFKBIL2

9 variants

Kenya

(African)

Children with IPD

173

Blood culture

Mortality (28%)

NS

Geldhoff, 2013, [55]

CARD8 NLRP1

NLRP3

rs2043211 rs11621270

rs35829479

Netherlands

(white)

Adults with BM

531 (72% PM)

CSF culture

Mortality (18%), unfavourable outcome: GOS 1–4 (38%), systemic complications, neurological complications

CARD8 rs2043211-TT genotype:

- Unfavourable outcome: p = 0.018, OR 2.19 (1.15–4.81)

- Systemic complications: p = 0.016, OR 2.48 (1.29–4.7)

- Neurological complications: p = 0.022, 3.03 (1.34–6.85)

NLRP1 rs11651270-TT genotype:

- Mortality: p = 0.047, OR 1.97 (1.02–3.85)

Cytokines

Schaaf, 2003, [21]

IL10

TNF

LTA

rs1800896 rs1800629 rs909253

Germany (white)

CAP and IPD

(age not specified)

69

CSF, blood, pleural fluid, sputum culture

Septic shock (19%), complications (48%), mortality (7%)

IL10-GG genotype:

- severity (development of septic shock): p = 0.008, OR 6.1 (1.4–27.2)

- complications and mortality: NS (re-calculated)

Schaaf, 2005, [23]

IL6

rs1800795

Germany (white)

CAP and IPD

(age not specified)

100

CSF, blood, pleural fluid, sputum culture

Bacterial dissemination (25%)

GG genotype: p = 0.04, OR 0.26 (0.07–0.94)

Carrol, 2011, [42]

IL-1Ra

rs4251961

Malawi

(African)

Children with IPD

299

Blood, sputum, CSF culture or Ag test or PCR

Mortality (22%)

NS

Doernberg, 2011, [41]

MIF

rs5844572rs755622

USA, Germany

(white)

Adults with IPD

30, 89

Culture from sterile body site

Disease phenotype: meningitis (14%)

Meningitis vs no meningitis:

- rs5844572–77 and 7X genotypes: p = 0.02, OR = 3.34 (1.34–8.35)

Martin- Loeches, 2012, [48]

IL6

rs1800795

Spain

(white)

Adults with CAP

306

Blood culture, urine antigen

ARDS (7%), septic shock (20%), multiple organ dysfunction syndrome (18%), hospital mortality (6%)

GG genotype: - ARDS: p = 0.002, OR = 0.25 (0.07–0.79)

- septic shock: p = 0.006, OR = 0.46 (0.18–0.79)

- multiple organ dysfunction syndrome: p = 0.02, OR = 0.53 (0.27–0.89)- survival (adjusted for age, gender, comorbidity, hospital of origin, and PSI): p = 0.048, OR = 0.27 (0.07–0.98)

Savva, 2016, [68]

MIF

rs5844572 rs755622

Netherlands

(white)

Adults with

BM

405

CSF culture

Unfavourable outcome- GOS 1–4 (33%), mortality (7%)

Unfavourable outcome:

- rs5844572–77 and 7X: p = 0.005, OR 1.89 (1.21–2.96)

- rs755622- GC and CC: p = 0.003, OR 1.9 (1.24–2.92)

Mortality:- rs5844572–77 and 7X: p = 0.03, OR 2.27 (1.07–4.83)

- rs755622 - GC and CC: p = 0.01, OR 2.6 (1.01–3.78)

Coagulation and fibrinolysis

Benfield, 2010, [39]

FVL

rs6025

Denmark

(unspecified)

Adults with IPD

163

Culture of CSF, blood or other sterile site

Mortality (15%),

ICU admission

NS

Brouwer, 2014, [60]

SERPINE1 (PAI-1)

rs1799889

Netherlands

(white)

Adults with BM

400

CSF culture

Unfavourable outcome- GOS 1–4 (33%), mortality (8%), cerebral infarction 14%), haemorrhages (2%)

5G/5G genotype (low expression):

- Unfavourable outcome: p = 0.035, OR 1.69 (1.03–2.78)

- Mortality: p = 0.039 OR 2.23 (1.02–4.86)

- Cerebral infarction: p = 0.011, OR 2.20 (1.19–4.07)

- Haemorrhages: p = 0.005, OR 9.94 (1.89–52.17)

Mook, 2015, [66]

CPB2 (TAFI)

rs1926447 rs3742264

Netherlands

(white)

Adults with BM

716

CSF culture

Unfavourable outcome – GOS 1–4 (29%), death (7%), systemic complications (31%)

Unfavourable outcome and death: NSSystemic complications:

- rs3742264 -AA allele vs common allele: p = 0.008, OR 0.40 (0.20–0.79)

Other

Eklund 2006, [28]

CRP

rs1800947 rs2794521

rs1130864

Finland

(white)

Patients with bacteraemia

42

Blood culture

Mortality (19%)

rs2794521- GG homozygotes: p = 0.03, OR 9.6 (1.3–72.5) recalculated

Payton, 2009, [36]

NOS2A

9 variants

Malawi

(African)

Children with IPD

229

Culture, PCR, antigen tests

Mortality (22%)

NS

Adriani, 2012, [51]

ADRB2

rs1042713 rs1042714

Netherlands

(mixed, 94% white)

Adults with BM

396

CSF culture

All BM unfavourable outcome: GOS 1–4 (23%), mortality (7%)

NS

Brouwer, 2012, [54]

GLCCI1

rs37972

Netherlands

(white)

Adults with BM

699

CSF culture

Treatment effect dexamethasone (mortality)

NS

Studies with genes in mixed categories

Lundbo, 2015, [67]

NFKBIE

NFKBIA

NFKBIL2

NFKBIZ

TIRAP

PTPN22

rs529948 rs3138053 rs2233406 rs760477 rs616597

rs8177374

rs2476601

Scandinavia, Germany (unspecified)

Children with BM / bacteraemia

372, 907

CSF or blood culture

30 day mortality (2%)

NS

Hypothesis free studies

Valls Seron, 2016, [69]

Exome array analysis

24,000 variants

Netherlands

(white)

Adults with BM

472

CSF culture

Unfavourable outcome: GOS 1–4 (32%), mortality (8%)

- AKT3 rs10157763 –A allele: p = 9.9 × 10− 5, OR 1.88 (1.4–2.6) - RAET1E rs3798763 and rs6925151 –G allele: p = 9.4 × 10− 5, OR 1.9 (1.4–2.6)- DCTN4 rs11954652 and rs6869603 –G allele: p = 2.4 × 10− 5, OR 5.6 (2.4–12.9)

Ferwerda, 2016, [72]

Sequencing of 46 genes

1385 variants

Netherlands

(white)

Adults with BM

435

CSF culture

Unfavourable outcome: GOS 1–4 (34%), mortality (8%)

- IRAK4 rs4251552 –G allele: p = 4.8 × 10− 4, OR 2.86 (1.58–5.18)- NOD2 rs2067085 –G allele: p = 5.1 × 10− 4, OR 2.16 (1.40–3.34)

Abbreviations: Ag agglutination, aHR adjusted Hazard ratio, ARDS acute respiratory stress syndrome, ARF Acute respiratory failure, BM bacterial meningitis, B-CAP bacterial-CAP, CAP community acquired pneumoniae, CI confidence interval, CSF cerebrospinal fluid, GOS Glasgow Outcome Scale, GWAS genome wide association study, ICU Intensive care unit, IPD invasive pneumococcal disease, MODS Multiple organ dysfunction syndrome, NS not significant, OR odds ratio, PCR polymerase chain reaction, PM pneumococcal meningitis

*Genetic variants: Synonyms of genetic variants can be found in Additional file 1: Table S1. † Results: None of the p-values are corrected for multiple testing

Twenty-eight studies (47%) were performed in adults (8188 patients) and 15 studies (25%) in children (4988 patients), 13 (22%) in all age categories (2675 patients) and 4 studies (7%) did not specify the age range of included patients. The population was limited to white patients in 39 studies (64%), mixed ethnicity in 9 studies (15%), and African origin in 3 studies (5%); ethnicity was not specified in 9 studies (15%). The sample size was less than 100 patients in 17 studies (28%), 100–500 patients in 40 studies (67%), and more than 500 in 3 studies (5%). The study population was defined by positive cultures of blood, cerebrospinal fluid or joint fluid in 41 studies (68%), and in 2 studies (3%) cultures of sputum or tracheal aspirate were included as well. Other studies used PCR, antigen tests or both (14 studies, 23%) to confirm bacterial presence. The control populations of the 57 susceptibility cohorts varied considerably and included healthy population-based controls, blood donors, participants in vaccine programs, patients from other hospital departments, university personnel or proxies and family members of patients. Some studies did specify if controls were ethnically, age or sex matched (32 cohorts, 56%).

Most studies (92%) had a candidate genetic variant approach looking at a selection of single nucleotide polymorphisms (range 1 to 326 polymorphisms; median 4). Five studies had a hypothesis free approach, including 1 genome wide association study, 2 exome wide association studies, and 2 sequencing studies [63, 69, 70, 71, 72]. Most studies (41; 68%) determined genotypes by PCR followed by various methods of allelic discrimination, of which 18 studies confirmed genotypes with sequencing, 3 studies with retesting of samples and 19 studies did not mention if or how genotypes were confirmed. Eleven studies (18%) used real time PCR (by Taqman® genotyping assays), 1 (2%) PCR with mass spectrometry analysis, and 7 (12%) next generation sequencing (12%) for determination of genotypes. Seven studies (12%) described blinding of laboratory personnel for the clinical information.

The χ2 test and/or Fisher’s exact test was used in 48 studies (80%) to compare genotypes of selected groups. Logistic regression with correction for confounders to compare genotype frequencies between selected groups was done in 31 studies (52%). Correction for multiple testing was used in 23 (66%) of the 35 studies that assessed three or more polymorphisms.

Meta-analysis

Meta-analysis could be done for 16 (combinations of) polymorphisms assessing an association with susceptibility and for 1 combination of polymorphisms assessing an association with outcome of pneumococcal disease. The number of cohorts in the meta-analysis varied between 2 and 10. Significant heterogeneity was found in 8 studies included in the meta-analyses for which therefore a random-effects model was used. Forest plots were made and overall ORs with 95% CIs were calculated (Additional file 2).

Candidate gene approach

Pathogen recognition receptor signalling pathways

Toll-like receptors (TLRs) or nod-like receptors (NRLs) are pathogen recognition receptors of the innate immune system that recognize molecular patterns derived from microbes. [77] Fourteen studies assessed the effect of polymorphisms in 11 genes of the TLR and NLR signalling pathways on pneumococcal disease [29, 30, 33, 43, 50, 53, 57, 59, 63, 65, 67, 72, 74, 76]. Six polymorphisms were assessed in multiple studies and could be included in a meta-analysis. Five studies assessed the association between polymorphisms in TLR2 (rs5743708) and TLR4 (rs4986790) and susceptibility [30, 33, 50, 59, 74]. In the meta-analyses neither of the polymorphisms showed any effect. Rs352140 in TLR9 was assessed in two studies for an association with susceptibility which resulted in no association in the separate studies and the meta-analysis [43, 74]. The CD14 CC genotype of rs2569190 was significantly associated with susceptibility in a meta-analysis including two studies (OR 1·77, 95% CI 1·18–2·66) [33, 59]. Two studies including 224 patients and 284 controls studied rs4251513 of IRAK4 and no effect was found on susceptibility in the meta-analysis [63, 65].

Polymorphisms in the Toll interleukin-1 receptor domain-containing adaptor protein (TIRAP) gene were investigated in three studies including five cohorts with in total 1601 white patients and 2826 African patients [29, 67, 76]. In the meta-analysis with the polymorphism rs8177374 was not associated with pneumococcal disease.

Three studies assessed the effect on outcome of polymorphisms in genes involved in pathogen recognition receptor signaling [50, 53, 65]. A Spanish study with 60 patients assessed the effect of 10 polymorphisms in IRAK4, IRAK1, IRAKM and MYD88 on outcome of pneumococcal disease, but after re-calculation of their results the patients groups appeared too small to find significant assocations [65]. A study of 66 children with pneumococcal meningitis on the influence of NOD1, NOD2, TLR2, TLR4, TLR9, TRAIL and CASP1 polymorphisms on susceptibility and outcome showed no significant associations after correction for multiple testing [53], [57].

Complement system

Mannose-binding lectin (MBL) is a soluble pattern recognition receptor of the collectin group that activates the lectin complement pathway after binding to a microorganism. Structural mutations in exon 1 of the MBL2 gene resulting in variant allele B, C or D (rs1800450, rs1800451 or rs5030737), have been associated with reduced functional serum MBL levels [78].

The effect of MBL2 variant allele B, C or D on susceptibility to pneumococcal disease was assessed in 9 studies which were included in the meta-analysis [18, 19, 27, 35, 49, 58, 62, 64, 76]. In the meta-analysis, 2504 patients and 4749 controls were included, and homozygosity of any of the variant alleles was significantly associated with susceptibility to pneumococcal disease (OR 1·67, 95% CI 1·04–2·69). A Funnel plot with the 10 study cohorts showed the overall effect on susceptibility was likely influenced by publication bias (Fig. 2). Effect on outcome of MBL2 variant allele B, C or D was assessed in 10 studies, but only 3 of these studies could be included in the meta-analysis due to lacking of detailed genotypic data in the manuscripts [35, 58, 64]. The meta-analysis showed no significant effect on outcome of pneumococcal disease. Rs7096206 in the promotor region of MBL2 was analysed in seven studies and yielded no significant association with susceptibility in the meta-analysis [18, 19, 27, 35, 49, 58, 62].
Fig. 2

Funnel plot with MBL2 studies. Funnel plot with studies assessing the effect of MBL2 variant allele B, C or D (rs1800450, rs1800451 or rs5030737) on pneumococcal disease susceptibility. Each dot represents one study. The vertical blue dashed line corresponds to the mean effect size on susceptibility. The outer dashed lines indicate the triangular region within which 95% of studies are expected to lie. SE: standard error as the measure of study size with a reversed scale (most powerful studies are placed towards the top), OR: odds ratio as the effect size of the studies on a log scale

After binding of MBL to a pathogens surface, a serine protease called MASP (MBL-associated serine protease) is activated, which cleaves complement precursors to activated complement proteins further down the cascade [79]. Associations of polymorphisms in MASP2 with pneumococcal disease were assessed in two studies, but showed no significant effect on outcome and susceptibility [34, 73].

Surfactant protein A or D (SFTPA, SFTPD) are also collectins and act as a first line of defence against microorganisms in the nasopharynx and respiratory tract by facilitating elimination of microorganisms [80]. A study of 7 SFTPD and SFTPA polymorphisms in 326 pneumococcal CAP patients and 1538 controls showed no association of these genes with susceptibility [44]. Another study of 182 European Americans (EA) and 53 African Americans (AA) with IPD assessed the effect on susceptibility of 24 polymorphisms in SFTPA and SFTPD [46]. Because genotypic data was not provided they could not be included in the meta-analysis. Their strongest associations were with two SFTPD polymorphisms (rs17886286 and rs12219080; OR 0.45, 95% CI 0.25–0.82and OR 0.32, 95% CI 0.13–0.78), not corrected for multiple testing [46].

L-Ficolin (encoded by FCN2) is a pattern-recognition molecule, that enhances phagocytosis and activates the lectin pathway of complement activation after binding to lipoteichoic acid or Gram-positive bacteria [81]. Five functional polymorphisms in FCN2 were analysed in 290 patients with pneumococcal disease and in 720 controls yielding no associations with susceptibility [31].

After initiation of the three complement activation pathways the final common pathway is activated, in which C5 is converted into C5a,an important anaphylatoxin and a chemoattractant [82]. A Dutch study with 217 pneumococcal meningitis patients assessed the effect on outcome of 17 polymorphisms in 7 complement components further down the cascade [47]. This yielded 1 significant association of rs17611 in C5 with unfavourable outcome (OR 2·25, 95% CI 1·33–3·81) after correction for multiple testing [47]. Another Dutch study investigated in the same population the effect of these complement components on susceptibility showing no significant associations after correction for multiple testing [56].

Fcγ receptors

Fc (fragment crystallizable) receptors are found on the surface of immune cells and bind to immunoglobulins (Ig). Of the 6 types of Fcγ receptors, FcγRIIa and FcγRIIIa exists as two allotypic variants with different binding affinity for IgG [83]. The more common F158 allotype of the FCGR3A gene has a lower IgG affinity than the V158 allotype (rs396991) [84]. For the FCGR2A gene the more common H131 allotype has a higher IgG affinity than the R131 allotype (rs1801274) [84]. Seven studies assessed the effect of rs1801274 (FCGR2A) on susceptibility and 3 assessed the effect on outcome of pneumococcal disease [17, 22, 24, 33, 37, 45, 50, 52]. The outcome studies lacked genotypic data for the meta-analysis and one study on susceptibility was excluded, because patient overlap with another study [22, 33]. In the meta-analysis on susceptibility 6 studies with a total of 570 patients and 4972 controls were included and no overall effect was found [17, 24, 33, 37, 45, 52]. One study assessed the effect of rs396991 (FCGR3A) in 85 bacteraemia pneumococcal pneumonia patients and 1224 healthy controls, showing no effect on susceptibility and outcome [45].

NFκβ signalling pathway

NFκB (nuclear factor kappa-light-chain-enhancer of activated B cells) is a transcriptional regulator important for both the adaptive and innate immune response [85]. Six studies investigated the effect of polymorphisms in genes coding for modulators of the NFκB signalling pathway on outcome and susceptibility of pneumococcal disease [32, 38, 40, 55, 67, 75]. Five polymorphisms in genes coding for NFκB inhibitors could be analysed in a meta-analysis. The effect of polymorphisms in NFKBIA and NFKBIE (rs3138053, rs2233406, rs529948) on susceptibility was assessed in two studies, revealing no significant associations in the meta-analyses [32, 67]. Two other polymorphisms in the NFκB inhibitor genes NFKBIZ (rs616597) and NFKBIL2 (rs760477) were assessed in 3 cohorts for an effect on susceptibility and meta-analysis showed no significant associations [38, 40, 67]. A study including 531 adult pneumococcal meningitis patients and 376 controls studied two polymorphisms in CARD8 and NLRP1 both coding for proteins required for activation of NFκB or caspases in the context for inflammation or apoptosis respectively [85]. This study showed an association of rs2043211 in CARD8 with poor outcome (OR 2·10, 95% CI 1·04–4·21) and rs11651270 in NLRP1 with death (OR 2·32, 95% CI 1·12–4·78), but this was not significant after correction for multiple testing [55].

Cytokines

Cytokines are important molecules mediating cell signalling and include small proteins like chemokines, interferons, interleukins (ILs), lymphokines, or tumor necrosis factors (TNFs) [86, 87] Seven studies assessed the effect of polymorphisms in 11 cytokine genes on susceptibility, disease phenotype and outcome of pneumococcal disease [21, 23, 41, 42, 48, 68, 75]. The polymorphism rs1800795 in IL6 was assessed in two studies, showing no effect on susceptibility in the meta-analysis [23, 48]. One Spanish study with 144 IPD patients and 280 controls assessed the effect on susceptibility of 33 polymorphisms in the genes coding for IL-10, IL-12B, IL-1A, IL-1B, IL-R1 and IL-4 [75]. None were significantly associated after correction for multiple testing [75].

Macrophage migrating inhibitory factor (MIF) is a pro-inflammatory cytokine acting at the interface of the immune and endocrine systems [88]. The effect of polymorphisms in MIF on pneumococcal disease were investigated in one phenotype study showing effect of the high expression allele (rs5844572) on developing the meningitis phenotype and one outcome study showing effect of high expression alleles (rs5844572, rs755622) on unfavourable outcome and death [41, 68].

Coagulation and fibrinolysis factors

During severe infection the inflammatory response shifts the haemostatic balance towards a pro-coagulant state, which can lead to diffuse intravascular coagulation and organ damage [89]. Three studies assessed the effect of polymorphisms in coagulation or fibrinolysis genes on susceptibility and outcome of pneumococcal disease [39, 60, 66]. A study investigated the effect of the factor V Leiden (FVL) mutation (rs6025) in 163 patients and 8147 controls on IPD susceptibility and outcome, showing no significant associations [39].

Carboxypeptidase B2 (CPB2), also known as thrombin-activatable fibrinolysis inhibitor (TAFI), plays an anti-fibrinolytic role during fibrin clot degradation and an anti-inflammatory role by inactivating pro-inflammatory mediators, such as complement activation products [90]. A study with 716 pneumococcal meningitis patients studied the effect of polymorphisms in carboxypeptidase B2 (CPB2, rs1926447, rs3742264) on disease susceptibility and outcome [66]. No effect was found on susceptibility, but rs3742264 was associated with developing systemic complications (OR 0·40, 95% CI 0·20–0·79) [66].

Plasminogen activator inhibitor 1 (PAI-1) inhibits the pro-fibrinolytic enzymes urokinase and tissue plasminogen activator and thereby modulates fibrinolysis [91]. The effect of rs1799889 in the gene coding for PAI-1 (SERPINE1) on pneumococcal meningitis outcome was studied in a Dutch study with 400 patients and they found an effect on occurrence of cerebral infarction (OR 2·20, 95% CI 1·19–4·07), unfavourable outcome (OR 1·69, 95% CI 1·03–2·78) and mortality (OR 2·20, 95% CI 1·02–4·86) [60].

Other factors

Eight studies focused on genes that could not be categorized in the other subcategories. Two of these studies assessed the role of polymorphisms in the gene coding for C-reactive protein (CRP) in pneumococcal disease. CRP contains a dinucleotide repeat polymorphism in the intron region (rs3138528) which was assessed in a study with 205 IPD patients and 345 controls, showing significantly more patients had the 134 base pair allele than controls (OR 1·52, 95% CI 1·18–1·96) [20]. Another study investigated the effect of 3 polymorphisms in CRP (rs1800947, rs2794521, rs1130864) on outcome in 42 patients with a pneumococcal bacteraemia and found an association with mortality and rs2794521 (OR 9·6, 95% CI 1·3–72·5), not corrected for multiple testing [28].

Protein thyrosine phosphatases (PTPs) regulate the immune response through influencing the responsiveness of B and T cell receptors [92]. Rs2476601in the gene coding for PTP non-receptor type 22 (PTPN22) was assessed in two studies with in total 1492 IPD patients and 2050 controls [25, 67]. The meta-analysis showed no effect on susceptibility [25].

Nitric oxide synthase 2 (NOS2) is an enzyme encoded by the NOS2 gene, which is involved in nitric oxide production and apoptosis of macrophages [93]. Nine polymorphisms in NOS2 were investigated in a Malawian study, showing no influence of any of the variants on IPD susceptibility or survival [36].

One study investigated if rs37972 in the glucocorticoid-induced transcript 1 gene (GLCCI1) influenced disease outcome and the response to glucocorticosteroids in pneumococcal meningitis [54]. The function of GLCCI1 unknown, but it is expressed in both lung cells and immune cells and may be an early marker of glucocorticoid-induced apoptosis [94]. No association was found between rs37972 and mortality rates per dexamethasone treatment group [54].

Studies have showed bacteria are able to hijack the β2-adrenoceptor and thereby stabilize its binding to the endothelium which could enhance crossing the blood-brain barrier [95]. The effect of 2 functional polymorphisms in the β2-adrenoceptor (ADRB2) gene on susceptibility and outcome of pneumococcal meningitis was studied in 396 patients and 376 controls [51]. Rs1042714 of ADRB2 was associated with susceptibility (OR 1·52, 95% CI 1·12–2·07) but had no influence on outcome of disease [51].

Studies with hypothesis free approach

Five studies had a hypothesis free approach to find (new) genetic variations associated with pneumococcal disease. Two of them were sequencing studies in a selected group of genes [63, 72]. The first study sequenced 3 genes involved in the Toll-like receptor signalling pathway: MYD88, IRAK4, IKBKG (inhibitor of nuclear factor kappa-B kinase subunit gamma) of 164 IPD patients and 164 controls [63]. After sequencing 233 variants were identified of which one (rs4251545 in IRAK4) had a minor allele frequency (MAF) of more than 5%. This variant was associated with susceptibility to IPD (OR 1·50; 95% CI 1·10–2·04; p = 9·96 × 10− 3) but after correction for multiple testing this polymorphism did not retain statistical significance [63].

The other sequencing study sequenced 46 innate immune genes of 435 patients and 416 controls to assess the influence on outcome and susceptibility to pneumococcal meningitis [72]. They identified 2099 variations of which 80% had a MAF below 1% (1854 variations for susceptibility and 1385 for outcome). Neither the single nucleotide polymorphism (SNP) or haplotype analysis nor the analysis for association between a set of rare variants and phenotypes, reached the significance level after correction for multiple testing. The strongest associations with susceptibility were in CARD8, rs2008521 (OR 1·82; CI 1·28–2·75; p = 8·2 × 10− 4) and in CXCL1, rs56078309 (OR 1·96; CI 1·34–2·87; p = 8·2 × 10− 4) and with outcome were in IRAK4, rs4251552 (OR 2.86; CI 1·58–5·18; p = 4·8 × 10− 4) and NOD2, rs2067085 (OR 2·16; CI 1·40–3·34; p = 5·1 × 10− 4) [72].

Two of the hypothesis free studies were exome wide association studies performed in the same Dutch cohort of pneumococcal meningitis patients [69, 71]. Genotyping of subjects in these studies was done with an Illumina BeadChip consisting of more than 240,000 markers, with approximately 75% of these markers having a MAF below 5%. The first study assessed susceptibility to pneumococcal meningitis and included 469 patients and 2072 controls and a total of 100,464 polymorphisms passed quality control thresholds [71]. The strongest associations with susceptibility were rs139064549 in COL11A1 (OR 3·21; 95% CI 2·05–5·02; p = 1·51 × 10− 6) and rs9309464 in EXOC6B (OR 0·66; 95% CI 0·54–0·81; p = 6·01 × 10− 5), both did not reach the exome wide significance level [71]. The study on outcome included 472 culture proven pneumococcal meningitis patients and their strongest association was in AKT3, rs10157763 (OR 1·88; 95% CI 1·4–2·6; p = 9·9 × 10− 5) but this was not significant after correction for multiple testing [69].

The fifth hypothesis free study was a genome wide association study on pneumococcal bacteraemia susceptibility in 429 Kenyan children and 2677 controls [70]. In this study samples were genotyped with an Affymetrix® SNP chip and polymorphisms not passing the quality control with a MAF of less than 1%, a HWE of p < 1 × 10− 20 and a missingness of more than 2%, were excluded for imputation. After sample and SNP quality control 787,861 genotyped autosomal SNPs were left for analysis, which were extended to 10,996,499 autosomal SNPs after imputation. The study identified an association which reached the genome wide significance threshold between rs140817150in a long intergenic non-coding RNA (lincRNA) gene (AC011288.2) and pneumococcal bacteraemia susceptibility and replicated the results in a replication cohort with 113 children and 1136 controls (OR 2·47, 95% CI 1·84–3·31, p-combined = 1·69 × 10− 9) [70].

Discussion

We identified 60 studies evaluating host genetic variations in 16,034 patients with pneumococcal disease. Meta-analyses showed that genetic variants in the genes CD14 (rs2569190) and MBL2 (one of the variant alleles rs1800450, rs1800451 or rs5030737) were associated with susceptibility to pneumococcal disease. A hypothesis free approach was applied in few studies resulting in one genome wide significant association in a gene coding for lincRNA (rs140817150) with IPD susceptibility which was replicated in an independent IPD cohort.

Few findings were replicated in independent cohorts. Replication generally led to negative results, or – in case of MBL2 – careful analysis suggested considerable publication bias. The role of genetic variation on outcome was evaluated in about half of identified studies, but results were not confirmed because of the lack of detailed clinical metadata and heterogeneity of definitions and outcomes. To ease replication, international collaboration between study groups on genetics in pneumococcal disease is needed to ensure uniform research designs and outcome measures [96, 97]. This should lead to an open source research register for genetic associations studies, evaluating host and pathogen genetic data of pneumococcal disease, to facilitate data exchange and prevent publication bias. Such team-science effort is needed to decrease methodological flaws and contribute to more robust findings on the genetic basis of pneumococcal disease, a disease with enormous impact on global health [1, 97].

The significantly associated polymorphisms in the meta-analysis, in CD14 (rs2569190) and MBL2 (one of the variant alleles of rs1800450, rs1800451 or rs5030737) are known functional polymorphisms. The variant alleles of MBL2 have structural differences which are associated with decreased MBL concentrations and thereby decreased activation of the complement system [98]. Soluble CD14 (sCD14) is a pattern recognition receptor and acts as a co-receptor of TLR-4 to bind microbial components to endothelial and epithelial cells [99]. The risk allele T of rs2569190 for pneumococcal disease susceptibility in our meta-analysis, is associated with high sCD14 levels in expression studies [100, 101]. Our findings correspond with other studies showing the T allele is associated with an increased occurrence of sepsis and increased serum sCD14 levels in patients with risk genotypes [102] [103]. Although the causal allele might be not the association signal due to linkage disequilibration, these studies are suggestive for a causal relationship of genetic variation in both MBL2 or CD14 and susceptibility to pneumococcal disease.

The results of our meta-analyses should be interpreted with caution because many included methodologically flawed studies. First of all, sample sizes were often inadequate, whereby robust conclusions on the influence of the studied genetic variants could not be drawn. In studies focusing on outcome, small sample sizes result in few unfavourable events per study group and consequentially limited study power. Second, in most studies data collection was retrospective which might have led to missing data. Many studies had a retrospective inclusion design which poses a risk for to selection bias as reflected by the extremely low mortality rates among included patients. In other studies DNA was not available for a considerable proportion of patients, particularly those with more severe disease passing away before DNA collection. Inclusion of patients with less severe disease decreases study power and could underestimate influence of polymorphisms on severity or mortality of pneumococcal disease. Third, case selection differed between studies. Different phenotypes of pneumococcal disease, ethnicities and age categories were studied which could possibly limit the meta-analysis. In 30% of the studies ethnicity was mixed or not specified, which could be a major source for bias since frequencies of polymorphic genetic loci vary substantially between ethnic groups. Furthermore, control populations were heterogeneously selected and only 8 cohorts (of 57 cohorts; 14%) matched for both age and sex. Fourth, quality control procedures for DNA extraction and genotyping were rarely specified. Only half of the studies which determined genotypes by PCR followed by allelic discrimination methods (21 of 41 studies) stated they confirmed genotypes by sequencing or retesting of samples. In the candidate gene studies only 15 (27%) described the genotyping success rate and 7 (13%) blinding of laboratory personal. Four out of the five hypothesis free studies described extensive quality control procedures like genotyping accuracy, calling rates, and rates of missing samples [69, 70, 71, 72]. Finally, statistical analyses differed between studies leading to different effect sizes or different cut-offs for significant associations. Logistic regression with correction for confounders was done in only half of the studies and about one third of the studies that assessed three or more polymorphisms did not correct for multiple testing.

In recent years, many loci have been identified by GWAS, since the cost of genotyping SNPs decreased and the cohort sizes increased [104]. Despite the success in identifying disease loci, understanding of how polymorphisms predispose individuals to disease remains limited [104]. Besides methodological flaws, it is likely single genes or genetic variants do not control susceptibility and outcome of complex traits. Probably most heritability can be explained by effects on genes outside core pathways due to interconnection with genes in regulatory networks expressed in disease-relevant cells [105]. In order to understand the genetics of complex traits future studies should focus on gene-gene interactions as well [97]. Other future approaches for increasing our understanding in heritability could be targeted or whole-genome sequencing in people with extreme phenotypes, in order to find variants in the lower frequency with larger effect domains [97]. Besides reference panels of genomic variation should be adequately used to enhance coverage of existing and future GWAS and methods for detection of copy number variants and other structural variants could be improved [97]. Besides all this, functional understanding of these variants is needed for better insight in pathogenesis of disease and drug discovery. For example the whole genome association study of the Kenyan Bacteraemia Study Group explored the functionality of a polymorphism in a gene coding for lincRNA, with a qPCR to quantify levels of RNA expression in leukocyte cell subtypes, observing elevation only in neutrophils [70]. Most of the studies included in this review investigated a functional role of identified polymorphisms by measuring serum of CSF protein expression, [20, 26, 28, 34, 41, 42, 44, 47, 55, 56, 58, 60, 66, 69, 72] but not all were able to demonstrate a functional effect. Moreover the majority of the studies (70%) did not analyse the functionality of the genetic variants.

Conclusions

Several host genetic polymorphisms have been identified to influence susceptibility and outcome of pneumococcal disease, but most of these studies are hampered by methodological flaws or were not reproduced (yet). Carefully designed whole-genome association and replication studies are needed with detailed clinical meta-data to further clarify and confirm the genetic basis of pneumococcal disease. To improve our understanding in the functionality of polymorphisms the next step is to investigate the downstream molecular effects of polymorphisms with large-scale clinical cohort studies within a specific acute illness as pneumococcal disease.

Notes

Acknowledgements

Not applicable.

Authors’ contributions

AK performed the search, study selection, data extraction and statistical analyses, and wrote the first draft of the manuscript. MB and DB conceived the study, provided funding and study supervision, and revised the final manuscript. All authors read and approved the final manuscript.

Funding

This study was supported by the Netherlands Organization for Health Research and Development (ZonMw; NWO-Vidi-Grant [917.17.308] to MB, NWO-Vidi-Grant [016.116.358] to DB) and the European Research Council (ERC Starting Grant to DB). The funding bodies had no role in the design of the study and collection, analysis, and interpretation of data and in writing the manuscript.

Ethics approval and consent to participate

Not applicable.

Consent for publication

Not applicable.

Competing interests

The authors declare that they have no competing interests.

Supplementary material

12920_2019_572_MOESM1_ESM.docx (13 kb)
Additional file 1: Table S1. Synonyms of genetic variants (DOCX 13 kb)
12920_2019_572_MOESM2_ESM.pdf (506 kb)
Additional file 2: Meta-analyses of genetic association studies on susceptibility and outcome of pneumococcal disease. (PDF 505 kb)

References

  1. 1.
    Mortality GBD. Causes of death C. global, regional, and national life expectancy, all-cause mortality, and cause-specific mortality for 249 causes of death, 1980-2015: a systematic analysis for the global burden of disease Study 2015. Lancet. 2016;388(10053):1459–544.CrossRefGoogle Scholar
  2. 2.
    Welte T, Torres A, Nathwani D. Clinical and economic burden of community-acquired pneumonia among adults in Europe. Thorax. 2012;67(1):71–9.PubMedCrossRefGoogle Scholar
  3. 3.
    Drijkoningen JJ, Rohde GG. Pneumococcal infection in adults: burden of disease. Clin Microbiol Infect. 2014;20(Suppl 5):45–51.PubMedCrossRefGoogle Scholar
  4. 4.
    Niederman MS. Community-acquired pneumonia: the U.S. perspective. Semin Respir Crit Care Med. 2009;30(2):179–88.PubMedCrossRefGoogle Scholar
  5. 5.
    Wahl B, O'Brien KL, Greenbaum A, Majumder A, Liu L, Chu Y, et al. Burden of Streptococcus pneumoniae and Haemophilus influenzae type b disease in children in the era of conjugate vaccines: global, regional, and national estimates for 2000-15. Lancet Glob Health. 2018;6(7):e744–e57.PubMedPubMedCentralCrossRefGoogle Scholar
  6. 6.
    Marrie TJ, Tyrrell GJ, Majumdar SR, Eurich DT. Effect of Age on the Manifestations and Outcomes of Invasive Pneumococcal Disease in Adults. Am J Med. 2018;131(1):100 e1–7.CrossRefGoogle Scholar
  7. 7.
    van de Beek D, Brouwer M, Hasbun R, Koedel U, Whitney CG, Wijdicks E. Community-acquired bacterial meningitis. Nat Rev Dis Primers. 2016;2:16074.Google Scholar
  8. 8.
    Lynch JP 3rd, Zhanel GG. Streptococcus pneumoniae: epidemiology, risk factors, and strategies for prevention. Semin Respir Crit Care Med. 2009;30(2):189–209.PubMedCrossRefGoogle Scholar
  9. 9.
    Bijlsma MW, Brouwer MC, Kasanmoentalib ES, Kloek AT, Lucas MJ, Tanck MW, et al. Community-acquired bacterial meningitis in adults in the Netherlands, 2006-14: a prospective cohort study. Lancet Infect Dis. 2016;16:339–47.PubMedCrossRefGoogle Scholar
  10. 10.
    LeBlanc JJ, ElSherif M, Ye L, MacKinnon-Cameron D, Li L, Ambrose A, et al. Burden of vaccine-preventable pneumococcal disease in hospitalized adults: a Canadian immunization research network (CIRN) serious outcomes surveillance (SOS) network study. Vaccine. 2017;35(29):3647–54.PubMedCrossRefGoogle Scholar
  11. 11.
    Thomas K, Mukkai Kesavan L, Veeraraghavan B, Jasmine S, Jude J, Shubankar M, et al. Invasive pneumococcal disease associated with high case fatality in India. J Clin Epidemiol. 2013;66(1):36–43.PubMedCrossRefGoogle Scholar
  12. 12.
    Robinson KA, Baughman W, Rothrock G, Barrett NL, Pass M, Lexau C, et al. Epidemiology of invasive Streptococcus pneumoniae infections in the United States, 1995-1998: opportunities for prevention in the conjugate vaccine era. JAMA. 2001;285(13):1729–35.PubMedCrossRefPubMedCentralGoogle Scholar
  13. 13.
    Brouwer MC, de Gans J, Heckenberg SG, Zwinderman AH, van der Poll T, van de Beek D. Host genetic susceptibility to pneumococcal and meningococcal disease: a systematic review and meta-analysis. Lancet InfectDis. 2009;9(1):31–44.CrossRefGoogle Scholar
  14. 14.
    Chapman SJ, Hill AV. Human genetic susceptibility to infectious disease. NatRevGenet. 2012;13(3):175–88.Google Scholar
  15. 15.
    Ludwig E, Bonanni P, Rohde G, Sayiner A, Torres A. The remaining challenges of pneumococcal disease in adults. Eur Respir Rev. 2012;21(123):57–65.PubMedCrossRefPubMedCentralGoogle Scholar
  16. 16.
    Review Manager (RevMan) Version 5.3 ed. Copenhagen: The Nordic Cochrane Centre, The Cochrane Collaboration; 2014.Google Scholar
  17. 17.
    Yee AM, Phan HM, Zuniga R, Salmon JE, Musher DM. Association between FcgammaRIIa-R131 allotype and bacteremic pneumococcal pneumonia. Clin Infect Dis. 2000;30(1):25–8.PubMedCrossRefPubMedCentralGoogle Scholar
  18. 18.
    Kronborg G, Weis N, Madsen HO, Pedersen SS, Wejse C, Nielsen H, et al. Variant mannose-binding lectin alleles are not associated with susceptibility to or outcome of invasive pneumococcal infection in randomly included patients. J Infect Dis. 2002;185(10):1517–20.PubMedCrossRefPubMedCentralGoogle Scholar
  19. 19.
    Roy S, Knox K, Segal S, Griffiths D, Moore CE, Welsh KI, et al. MBL genotype and risk of invasive pneumococcal disease: a case-control study. Lancet. 2002;359(9317):1569–73.PubMedCrossRefPubMedCentralGoogle Scholar
  20. 20.
    Roy S, Hill AV, Knox K, Griffiths D, Crook D. Research pointers: association of common genetic variant with susceptibility to invasive pneumococcal disease. BMJ. 2002;324(7350):1369.PubMedPubMedCentralCrossRefGoogle Scholar
  21. 21.
    Schaaf BM, Boehmke F, Esnaashari H, Seitzer U, Kothe H, Maass M, et al. Pneumococcal septic shock is associated with the interleukin-10-1082 gene promoter polymorphism. Am J Respir Crit Care Med. 2003;168(4):476–80.PubMedCrossRefPubMedCentralGoogle Scholar
  22. 22.
    Yuan FF, Wong M, Pererva N, Keating J, Davis AR, Bryant JA, et al. FcgammaRIIA polymorphisms in Streptococcus pneumoniae infection. Immunol Cell Biol. 2003;81(3):192–5.PubMedCrossRefPubMedCentralGoogle Scholar
  23. 23.
    Schaaf B, Rupp J, Muller-Steinhardt M, Kruse J, Boehmke F, Maass M, et al. The interleukin-6 -174 promoter polymorphism is associated with extrapulmonary bacterial dissemination in Streptococcus pneumoniae infection. Cytokine. 2005;31(4):324–8.PubMedCrossRefPubMedCentralGoogle Scholar
  24. 24.
    Moens L, Van Hoeyveld E, Verhaegen J, De Boeck K, Peetermans WE, Bossuyt X. Fcgamma-receptor IIA genotype and invasive pneumococcal infection. Clin Immunol. 2006;118(1):20–3.PubMedCrossRefPubMedCentralGoogle Scholar
  25. 25.
    Chapman SJ, Khor CC, Vannberg FO, Maskell NA, Davies CW, Hedley EL, et al. PTPN22 and invasive bacterial disease. Nat Genet. 2006;38(5):499–500.PubMedCrossRefPubMedCentralGoogle Scholar
  26. 26.
    Perez-Castellano M, Penaranda M, Payeras A, Mila J, Riera M, Vidal J, et al. Mannose-binding lectin does not act as an acute-phase reactant in adults with community-acquired pneumococcal pneumonia. Clin Exp Immunol. 2006;145(2):228–34.PubMedPubMedCentralCrossRefGoogle Scholar
  27. 27.
    Moens L, Van Hoeyveld E, Peetermans WE, De Boeck C, Verhaegen J, Bossuyt X. Mannose-binding lectin genotype and invasive pneumococcal infection. Hum Immunol. 2006;67(8):605–11.PubMedCrossRefPubMedCentralGoogle Scholar
  28. 28.
    Eklund C, Huttunen R, Syrjanen J, Laine J, Vuento R, Hurme M. Polymorphism of the C-reactive protein gene is associated with mortality in bacteraemia. Scand J Infect Dis. 2006;38(11–12):1069–73.PubMedCrossRefPubMedCentralGoogle Scholar
  29. 29.
    Khor CC, Chapman SJ, Vannberg FO, Dunne A, Murphy C, Ling EY, et al. A mal functional variant is associated with protection against invasive pneumococcal disease, bacteremia, malaria and tuberculosis. Nat Genet. 2007;39(4):523–8.PubMedPubMedCentralCrossRefGoogle Scholar
  30. 30.
    Moens L, Verhaegen J, Pierik M, Vermeire S, De Boeck K, Peetermans WE, et al. Toll-like receptor 2 and toll-like receptor 4 polymorphisms in invasive pneumococcal disease. Microbes Infect. 2007;9(1):15–20.PubMedCrossRefPubMedCentralGoogle Scholar
  31. 31.
    Chapman SJ, Vannberg FO, Khor CC, Segal S, Moore CE, Knox K, et al. Functional polymorphisms in the FCN2 gene are not associated with invasive pneumococcal disease. Mol Immunol. 2007;44(12):3267–70.PubMedCrossRefGoogle Scholar
  32. 32.
    Chapman SJ, Khor CC, Vannberg FO, Frodsham A, Walley A, Maskell NA, et al. IkappaB genetic polymorphisms and invasive pneumococcal disease. Am J Respir Crit Care Med. 2007;176(2):181–7.PubMedCrossRefPubMedCentralGoogle Scholar
  33. 33.
    Yuan FF, Marks K, Wong M, Watson S, de Leon E, McIntyre PB, et al. Clinical relevance of TLR2, TLR4, CD14 and FcgammaRIIA gene polymorphisms in Streptococcus pneumoniae infection. Immunol Cell Biol. 2008;86(3):268–70.PubMedCrossRefGoogle Scholar
  34. 34.
    Garcia-Laorden MI, Sole-Violan J, Rodriguez de Castro F, Aspa J, Briones ML, Garcia-Saavedra A, et al. Mannose-binding lectin and mannose-binding lectin-associated serine protease 2 in susceptibility, severity, and outcome of pneumonia in adults. J Allergy Clin Immunol. 2008;122(2):368–74–74 e1–2.CrossRefGoogle Scholar
  35. 35.
    Endeman H, Herpers BL, de Jong BAW, Voorn GP, Grutters JC, van Velzen-Blad H, et al. Mannose-binding lectin genotypes in susceptibility to community-acquired pneumonia. Chest. 2008;134(6):1135–40.PubMedCrossRefGoogle Scholar
  36. 36.
    Payton A, Payne D, Mankhambo LA, Banda DL, Hart CA, Ollier WE, et al. Nitric oxide synthase 2A (NOS2A) polymorphisms are not associated with invasive pneumococcal disease. BMC Med Genet. 2009;10:28.PubMedPubMedCentralCrossRefGoogle Scholar
  37. 37.
    Endeman H, Cornips MC, Grutters JC, van den Bosch JM, Ruven HJ, van Velzen-Blad H, et al. The Fcgamma receptor IIA-R/R131 genotype is associated with severe sepsis in community-acquired pneumonia. Clin Vaccine Immunol. 2009;16(7):1087–90.PubMedPubMedCentralCrossRefGoogle Scholar
  38. 38.
    Chapman SJ, Khor CC, Vannberg FO, Rautanen A, Segal S, Moore CE, et al. NFKBIZ polymorphisms and susceptibility to pneumococcal disease in European and African populations. Genes Immun. 2010;11(4):319–25.PubMedCrossRefPubMedCentralGoogle Scholar
  39. 39.
    Benfield T, Ejrnaes K, Juul K, Ostergaard C, Helweg-Larsen J, Weis N, et al. Influence of factor V Leiden on susceptibility to and outcome from critical illness: a genetic association study. Crit Care. 2010;14(2):R28.PubMedPubMedCentralCrossRefGoogle Scholar
  40. 40.
    Chapman SJ, Khor CC, Vannberg FO, Rautanen A, Walley A, Segal S, et al. Common NFKBIL2 polymorphisms and susceptibility to pneumococcal disease: a genetic association study. Crit Care. 2010;14(6):R227.PubMedPubMedCentralCrossRefGoogle Scholar
  41. 41.
    Doernberg S, Schaaf B, Dalhoff K, Leng L, Beitin A, Quagliarello V, et al. Association of macrophage migration inhibitory factor (MIF) polymorphisms with risk of meningitis from Streptococcus pneumoniae. Cytokine. 2011;53(3):292–4.PubMedPubMedCentralCrossRefGoogle Scholar
  42. 42.
    Carrol ED, Payton A, Payne D, Miyajima F, Chaponda M, Mankhambo LA, et al. The IL1RN promoter rs4251961 correlates with IL-1 receptor antagonist concentrations in human infection and is differentially regulated by GATA-1. J Immunol. 2011;186(4):2329–35.PubMedCrossRefGoogle Scholar
  43. 43.
    Sanders MS, van Well GT, Ouburg S, Lundberg PS, van Furth AM, Morre SA. Single nucleotide polymorphisms in TLR9 are highly associated with susceptibility to bacterial meningitis in children. Clin Infect Dis. 2011;52(4):475–80.PubMedCrossRefGoogle Scholar
  44. 44.
    Garcia-Laorden MI, Rodriguez de Castro F, Sole-Violan J, Rajas O, Blanquer J, Borderias L, et al. Influence of genetic variability at the surfactant proteins A and D in community-acquired pneumonia: a prospective, observational, genetic study. Crit Care. 2011;15(1):R57.PubMedPubMedCentralCrossRefGoogle Scholar
  45. 45.
    Sole-Violan J, Garcia-Laorden MI, Marcos-Ramos JA, de Castro FR, Rajas O, Borderias L, et al. The Fcgamma receptor IIA-H/H131 genotype is associated with bacteremia in pneumococcal community-acquired pneumonia. Crit Care Med. 2011;39(6):1388–93.PubMedCrossRefGoogle Scholar
  46. 46.
    Lingappa JR, Dumitrescu L, Zimmer SM, Lynfield R, McNicholl JM, Messonnier NE, et al. Identifying host genetic risk factors in the context of public health surveillance for invasive pneumococcal disease. PLoS One. 2011;6(8):e23413.PubMedPubMedCentralCrossRefGoogle Scholar
  47. 47.
    Woehrl B, Brouwer MC, Murr C, Heckenberg SG, Baas F, Pfister HW, et al. Complement component 5 contributes to poor disease outcome in humans and mice with pneumococcal meningitis. JClinInvest. 2011;121(10):3943–53.Google Scholar
  48. 48.
    Martin-Loeches I, Sole-Violan J. Rodriguez de Castro F, Garcia-Laorden MI, Borderias L, Blanquer J, et al. variants at the promoter of the interleukin-6 gene are associated with severity and outcome of pneumococcal community-acquired pneumonia. Intensive Care Med. 2012;38(2):256–62.PubMedCrossRefPubMedCentralGoogle Scholar
  49. 49.
    Garcia-Laorden MI. Rodriguez de Castro F, Sole-Violan J, Payeras a, Briones ML, Borderias L, et al. the role of mannose-binding lectin in pneumococcal infection. Eur Respir J. 2013;41(1):131–9.PubMedCrossRefGoogle Scholar
  50. 50.
    Garnacho-Montero J, Garcia-Cabrera E, Jimenez-Alvarez R, Diaz-Martin A, Revuelto-Rey J, Aznar-Martin J, et al. Genetic variants of the MBL2 gene are associated with mortality in pneumococcal sepsis. Diagn Microbiol Infect Dis. 2012;73(1):39–44.PubMedCrossRefPubMedCentralGoogle Scholar
  51. 51.
    Adriani KS, Brouwer MC, Baas F, Zwinderman AH, van der Ende A, van de Beek D. Genetic variation in the beta2-Adrenocepter gene is associated with susceptibility to bacterial meningitis in adults. PLoSOne. 2012;7(5):e37618.CrossRefGoogle Scholar
  52. 52.
    Bougle A, Max A, Mongardon N, Grimaldi D, Pene F, Rousseau C, et al. Protective effects of FCGR2A polymorphism in invasive pneumococcal diseases. Chest. 2012;142(6):1474–81.PubMedCrossRefPubMedCentralGoogle Scholar
  53. 53.
    van Well GT, Sanders MS, Ouburg S, van Furth AM, Morre SA. Polymorphisms in toll-like receptors 2, 4, and 9 are highly associated with hearing loss in survivors of bacterial meningitis. PLoS One. 2012;7(5):e35837.PubMedPubMedCentralCrossRefGoogle Scholar
  54. 54.
    Brouwer MC, van der Ende A, Baas F, van de Beek D. Genetic variation in GLCCI1 and dexamethasone in bacterial meningitis. J Infect. 2012;65(5):465–7.PubMedCrossRefPubMedCentralGoogle Scholar
  55. 55.
    Geldhoff M, Mook-Kanamori BB, Brouwer MC, Valls SM, Baas F, van der Ende A, et al. Genetic variation in inflammasome genes is associated with outcome in bacterial meningitis. Immunogenetics. 2013;65(1):9–16.PubMedCrossRefGoogle Scholar
  56. 56.
    Adriani KS, Brouwer MC, Geldhoff M, Baas F, Zwinderman AH, Paul Morgan B, et al. Common polymorphisms in the complement system and susceptiblity to bacterial meningitis. J Infect. 2013;66(3):255–62.PubMedCrossRefPubMedCentralGoogle Scholar
  57. 57.
    van Well GT, Sanders MS, Ouburg S, Kumar V, van Furth AM, Morre SA. Single nucleotide polymorphisms in pathogen recognition receptor genes are associated with susceptibility to meningococcal meningitis in a pediatric cohort. PLoS One. 2013;8(5):e64252.PubMedPubMedCentralCrossRefGoogle Scholar
  58. 58.
    Brouwer MC, Baas F, van der Ende A, van de Beek D. Genetic variation and cerebrospinal fluid levels of mannose binding lectin in pneumococcal meningitis patients. PLoS One. 2013;8(5):e65151.PubMedPubMedCentralCrossRefGoogle Scholar
  59. 59.
    Telleria-Orriols JJ, Garcia-Salido A, Varillas D, Serrano-Gonzalez A, Casado-Flores J. TLR2-TLR4/CD14 polymorphisms and predisposition to severe invasive infections by Neisseria meningitidis and Streptococcus pneumoniae. Med Int. 2014;38(6):356–62.Google Scholar
  60. 60.
    Brouwer MC, Meijers JC, Baas F, van der Ende A, Pfister HW, Giese A, et al. Plasminogen activator inhibitor-1 influences cerebrovascular complications and death in pneumococcal meningitis. Acta Neuropathol. 2014;127(4):553–64.PubMedCrossRefGoogle Scholar
  61. 61.
    Munoz-Almagro C, Bautista C, Arias MT, Boixeda R, Del Amo E, Borras C, et al. High prevalence of genetically-determined mannose binding lectin deficiency in young children with invasive pneumococcal disease. Clin Microbiol Infect. 2014;20(10):O745–52.PubMedCrossRefGoogle Scholar
  62. 62.
    Lundbo LF, Harboe ZB, Clausen LN, Hollegaard MV, Sorensen HT, Hougaard DM, et al. Mannose-binding lectin gene, MBL2, polymorphisms are not associated with susceptibility to invasive pneumococcal disease in children. Clin Infect Dis. 2014;59(4):e66–71.PubMedCrossRefGoogle Scholar
  63. 63.
    Ellis MK, Elliott KS, Rautanen A, Crook DW, Hill AV, Chapman SJ. Rare variants in MYD88, IRAK4 and IKBKG and susceptibility to invasive pneumococcal disease: a population-based case-control study. PLoS One. 2015;10(4):e0123532.PubMedPubMedCentralCrossRefGoogle Scholar
  64. 64.
    Mills TC, Chapman S, Hutton P, Gordon AC, Bion J, Chiche JD, et al. Variants in the mannose-binding lectin gene MBL2 do not associate with Sepsis susceptibility or survival in a large European cohort. Clin Infect Dis. 2015;61(5):695–703.PubMedPubMedCentralCrossRefGoogle Scholar
  65. 65.
    Carrasco-Colom J, Jordan I, Alsina L, Garcia-Garcia JJ, Cambra-Lasaosa FJ, Martin-Mateos MA, et al. Association of Polymorphisms in IRAK1, IRAK4 and MyD88, and severe invasive pneumococcal disease. Pediatr Infect Dis J. 2015;34(9):1008–13.PubMedCrossRefGoogle Scholar
  66. 66.
    Mook-Kanamori BB, Valls Seron M, Geldhoff M, Havik SR, van der Ende A, Baas F, et al. Thrombin-activatable fibrinolysis inhibitor influences disease severity in humans and mice with pneumococcal meningitis. J Thromb Haemost. 2015;13(11):2076–86.PubMedCrossRefGoogle Scholar
  67. 67.
    Lundbo LF, Harboe ZB, Clausen LN, Hollegaard MV, Sorensen HT, Hougaard DM, et al. Genetic variation in NFKBIE is associated with increased risk of pneumococcal meningitis in children. EBioMedicine. 2016;3:93–9.PubMedCrossRefGoogle Scholar
  68. 68.
    Savva A, Brouwer MC, Roger T, Valls Seron M, Le Roy D, Ferwerda B, et al. Functional polymorphisms of macrophage migration inhibitory factor as predictors of morbidity and mortality of pneumococcal meningitis. Proc Natl Acad Sci U S A. 2016;113(13):3597–602.PubMedPubMedCentralCrossRefGoogle Scholar
  69. 69.
    Valls Seron M, Ferwerda B, Engelen-Lee J, Geldhoff M, Jaspers V, Zwinderman AH, et al. V-AKT murine thymoma viral oncogene homolog 3 (AKT3) contributes to poor disease outcome in humans and mice with pneumococcal meningitis. Acta Neuropathol Commun. 2016;4(1):50.PubMedPubMedCentralCrossRefGoogle Scholar
  70. 70.
    Kenyan Bacteraemia Study G, Wellcome Trust Case Control C, Rautanen A, Pirinen M, Mills TC, Rockett KA, et al. Polymorphism in a lincRNA associates with a doubled risk of pneumococcal bacteremia in Kenyan children. Am J Hum Genet. 2016;98(6):1092–100.CrossRefGoogle Scholar
  71. 71.
    Kloek AT, van Setten J, van der Ende A, Bots ML, Asselbergs FW, Valls Seron M, et al. Exome Array analysis of susceptibility to pneumococcal meningitis. Sci Rep. 2016;6:29351.PubMedPubMedCentralCrossRefGoogle Scholar
  72. 72.
    Ferwerda B, Valls Seron M, Jongejan A, Zwinderman AH, Geldhoff M, van der Ende A, et al. Variation of 46 innate immune genes evaluated for their contribution in pneumococcal meningitis susceptibility and outcome. EBioMedicine. 2016;10:77–84.PubMedPubMedCentralCrossRefGoogle Scholar
  73. 73.
    Kasanmoentalib ES, Valls Seron M, Ferwerda B, Tanck MW, Zwinderman AH, Baas F, et al. Mannose-binding lectin-associated serine protease 2 (MASP-2) contributes to poor disease outcome in humans and mice with pneumococcal meningitis. J Neuroinflammation. 2017;14(1):2.PubMedPubMedCentralCrossRefGoogle Scholar
  74. 74.
    Gowin E, Swiatek-Koscielna B, Kaluzna E, Nowak J, Michalak M, Wysocki J, et al. Analysis of TLR2, TLR4, and TLR9 single nucleotide polymorphisms in children with bacterial meningitis and their healthy family members. Int J Infect Dis. 2017;60:23–8.PubMedCrossRefGoogle Scholar
  75. 75.
    Sangil A, Arranz MJ, Guerri-Fernandez R, Perez M, Monzon H, Payeras A, et al. Genetic susceptibility to invasive pneumococcal disease. Infect Genet Evol. 2018;59:126–31.PubMedCrossRefGoogle Scholar
  76. 76.
    Gowin E, Swiatek-Koscielna B, Kaluzna E, Strauss E, Wysocki J, Nowak J, et al. How many single-nucleotide polymorphisms (SNPs) must be tested in order to prove susceptibility to bacterial meningitis in children? Analysis of 11 SNPs in seven genes involved in the immune response and their effect on the susceptibility to bacterial meningitis in children. Innate Immun. 2018;24(3):163–70.PubMedCrossRefGoogle Scholar
  77. 77.
    Kumar H, Kawai T, Akira S. Pathogen recognition by the innate immune system. Int Rev Immunol. 2011;30(1):16–34.PubMedCrossRefGoogle Scholar
  78. 78.
    Garred P, Genster N, Pilely K, Bayarri-Olmos R, Rosbjerg A, Ma YJ, et al. A journey through the lectin pathway of complement-MBL and beyond. Immunol Rev. 2016;274(1):74–97.PubMedCrossRefGoogle Scholar
  79. 79.
    Matsushita M, Endo Y, Fujita T. Structural and functional overview of the lectin complement pathway: its molecular basis and physiological implication. Arch Immunol Ther Exp. 2013;61(4):273–83.CrossRefGoogle Scholar
  80. 80.
    Ujma S, Horsnell WG, Katz AA, Clark HW, Schafer G. Non-pulmonary immune functions of surfactant proteins a and D. J Innate Immun. 2017;9(1):3–11.PubMedCrossRefPubMedCentralGoogle Scholar
  81. 81.
    Endo Y, Matsushita M, Fujita T. New insights into the role of ficolins in the lectin pathway of innate immunity. Int Rev Cell Mol Biol. 2015;316:49–110.PubMedCrossRefPubMedCentralGoogle Scholar
  82. 82.
    Yan C, Gao H. New insights for C5a and C5a receptors in sepsis. Front Immunol. 2012;3:368.PubMedPubMedCentralCrossRefGoogle Scholar
  83. 83.
    Pincetic A, Bournazos S, DiLillo DJ, Maamary J, Wang TT, Dahan R, et al. Type I and type II fc receptors regulate innate and adaptive immunity. Nat Immunol. 2014;15(8):707–16.PubMedCrossRefPubMedCentralGoogle Scholar
  84. 84.
    Hargreaves CE, Rose-Zerilli MJ, Machado LR, Iriyama C, Hollox EJ, Cragg MS, et al. Fcgamma receptors: genetic variation, function, and disease. Immunol Rev. 2015;268(1):6–24.PubMedCrossRefPubMedCentralGoogle Scholar
  85. 85.
    Hayden MS, West AP, Ghosh S. NF-kappaB and the immune response. Oncogene. 2006;25(51):6758–80.PubMedCrossRefPubMedCentralGoogle Scholar
  86. 86.
    Faix JD. Biomarkers of sepsis. Crit Rev Clin Lab Sci. 2013;50(1):23–36.PubMedPubMedCentralCrossRefGoogle Scholar
  87. 87.
    Hanada T, Yoshimura A. Regulation of cytokine signaling and inflammation. Cytokine Growth Factor Rev. 2002;13(4–5):413–21.PubMedCrossRefPubMedCentralGoogle Scholar
  88. 88.
    Calandra T. Macrophage migration inhibitory factor and host innate immune responses to microbes. Scand J Infect Dis. 2003;35(9):573–6.PubMedCrossRefPubMedCentralGoogle Scholar
  89. 89.
    Levi M, Poll T. Coagulation in patients with severe sepsis. Semin Thromb Hemost. 2015;41(1):9–15.PubMedCrossRefPubMedCentralGoogle Scholar
  90. 90.
    Campbell WD, Lazoura E, Okada N, Okada H. Inactivation of C3a and C5a octapeptides by carboxypeptidase R and carboxypeptidase N. MicrobiolImmunol. 2002;46(2):131–4.Google Scholar
  91. 91.
    Iwaki T, Urano T, Umemura K. PAI-1, progress in understanding the clinical problem and its aetiology. BrJHaematol. 2012;157(3):291–8.Google Scholar
  92. 92.
    Stanford SM, Rapini N, Bottini N. Regulation of TCR signalling by tyrosine phosphatases: from immune homeostasis to autoimmunity. Immunology. 2012;137(1):1–19.PubMedPubMedCentralCrossRefGoogle Scholar
  93. 93.
    Marriott HM, Ali F, Read RC, Mitchell TJ, Whyte MK, Dockrell DH. Nitric oxide levels regulate macrophage commitment to apoptosis or necrosis during pneumococcal infection. FASEB J. 2004;18(10):1126–8.PubMedCrossRefPubMedCentralGoogle Scholar
  94. 94.
    Tantisira KG, Lasky-Su J, Harada M, Murphy A, Litonjua AA, Himes BE, et al. Genomewide association between GLCCI1 and response to glucocorticoid therapy in asthma. N Engl J Med. 2011;365(13):1173–83.PubMedPubMedCentralCrossRefGoogle Scholar
  95. 95.
    Coureuil M, Lecuyer H, Scott MG, Boularan C, Enslen H, Soyer M, et al. Meningococcus hijacks a beta2-adrenoceptor/beta-Arrestin pathway to cross brain microvasculature endothelium. Cell. 2010;143(7):1149–60.PubMedCrossRefPubMedCentralGoogle Scholar
  96. 96.
    van de Beek D. Progress and challenges in bacterial meningitis. Lancet. 2012;380(9854):1623–4.PubMedCrossRefPubMedCentralGoogle Scholar
  97. 97.
    Manolio TA, Collins FS, Cox NJ, Goldstein DB, Hindorff LA, Hunter DJ, et al. Finding the missing heritability of complex diseases. Nature. 2009;461(7265):747–53.PubMedPubMedCentralCrossRefGoogle Scholar
  98. 98.
    Turner MW, Hamvas RM. Mannose-binding lectin: structure, function, genetics and disease associations. Rev Immunogenet. 2000;2(3):305–22.PubMedPubMedCentralGoogle Scholar
  99. 99.
    Landmann R, Reber AM, Sansano S, Zimmerli W. Function of soluble CD14 in serum from patients with septic shock. J Infect Dis. 1996;173(3):661–8.PubMedCrossRefGoogle Scholar
  100. 100.
    Nieto-Fontarigo JJ, Salgado FJ, San-Jose ME, Cruz MJ, Casas-Fernandez A, Gomez-Conde MJ, et al. The CD14 (−159 C/T) SNP is associated with sCD14 levels and allergic asthma, but not with CD14 expression on monocytes. Sci Rep. 2018;8(1):4147.PubMedPubMedCentralCrossRefGoogle Scholar
  101. 101.
    Keskin O, Birben E, Sackesen C, Soyer OU, Alyamac E, Karaaslan C, et al. The effect of CD14-c159T genotypes on the cytokine response to endotoxin by peripheral blood mononuclear cells from asthmatic children. Ann Allergy Asthma Immunol. 2006;97(3):321–8.PubMedCrossRefPubMedCentralGoogle Scholar
  102. 102.
    Fan WC, Liu CW, Ou SM, Huang CC, Li TH, Lee KC, et al. TLR4/CD14 variants-related serologic and immunologic Dys-regulations predict severe Sepsis in febrile De-compensated cirrhotic patients. PLoS One. 2016;11(11):e0166458.PubMedPubMedCentralCrossRefGoogle Scholar
  103. 103.
    Wu Q, Xu X, Ren J, Liu S, Liao X, Wu X, et al. Association between the -159C/T polymorphism in the promoter region of the CD14 gene and sepsis: a meta-analysis. BMC Anesthesiol. 2017;17(1):11.PubMedPubMedCentralCrossRefGoogle Scholar
  104. 104.
    Wijmenga C, Zhernakova A. The importance of cohort studies in the post-GWAS era. Nat Genet. 2018;50(3):322–8.PubMedCrossRefPubMedCentralGoogle Scholar
  105. 105.
    Boyle EA, Li YI, Pritchard JK. An expanded view of complex traits: from polygenic to Omnigenic. Cell. 2017;169(7):1177–86.PubMedPubMedCentralCrossRefGoogle Scholar

Copyright information

© The Author(s). 2019

Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.

Authors and Affiliations

  1. 1.Department of Neurology, Amsterdam NeuroscienceAmsterdam UMC, University of AmsterdamAmsterdamThe Netherlands

Personalised recommendations