Literature DB >> 21881118

Genetic variants and susceptibility to neurological complications following West Nile virus infection.

Mark Loeb1, Sasha Eskandarian, Mark Rupp, Neil Fishman, Leanne Gasink, Jan Patterson, Jonathan Bramson, Thomas J Hudson, Mathieu Lemire.   

Abstract

To determine genetic factors predisposing to neurological complications following West Nile virus infection, we analyzed a cohort of 560 neuroinvasive case patients and 950 control patients for 13 371 mostly nonsynonymous single-nucleotide polymorphisms (SNPs). The top 3 SNPs on the basis of statistical significance were also in genes of biological plausibility: rs2066786 in RFC1 (replication factor C1) (P = 1.88 × 10(-5); odds ratio [OR], 0.68 [95% confidence interval {CI}, .56-.81]); rs2298771 in SCN1A (sodium channel, neuronal type I α subunit) (P = 5.87 × 10(-5); OR, 1.47 [95% CI, 1.21-1.77]); and rs25651 in ANPEP (ananyl aminopeptidase) (P = 1.44 × 10(-4); OR, 0.69 [95% CI, .56-.83]). Additional genotyping of these SNPs in a separate sample of 264 case patients and 296 control patients resulted in a lack of significance in the replication cohort; joint significance was as follows: rs2066786, P = .0022; rs2298771, P = .005; rs25651, P = .042. Using mostly nonsynonymous variants, we therefore did not identify genetic variants associated with neuroinvasive disease.

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Year:  2011        PMID: 21881118      PMCID: PMC3203390          DOI: 10.1093/infdis/jir493

Source DB:  PubMed          Journal:  J Infect Dis        ISSN: 0022-1899            Impact factor:   5.226


Over the past decade, West Nile virus (WNV) has emerged as an important human pathogen in North America, where it has been reported in a majority of states and provinces in the United States and Canada [1-3]. Of those infected, approximately 20% develop mild symptoms (“West Nile fever,” eg, fever, malaise, headache, myalgia, rash) [4] and about 1 in 150 develop meningitis or encephalitis [4, 5]. The incidence of severe neurological syndromes increases with age and includes encephalitis, meningitis, acute flaccid paralysis, peripheral neuropathy, polyradiculopathy, optic neuritis, and acute demyelinating encephalitis [5-9]. Pathological changes within the central nervous system appear to be due to several factors, including the direct result of viral proliferation within neuronal and glial cells, cytotoxic immune response to infected cells, diffuse perivascular inflammation, and microglial nodule formation [10-12]. Given the fact that only a small minority of those infected develop severe disease and the fact that risk factors, apart from older age and immunosuppression [1, 13–15], are not well defined, there is a strong rationale to suspect a genetic predisposition to WNV neurological complications. Several putative susceptibility genes have been reported in single candidate gene studies. An association between symptomatic WNV disease and homozygosity for the CCR5Δ32 mutation in the chemokine receptor gene CCR5 was initially reported [16, 17]. More recently, this association was not replicated, but a study suggestive of a link to clinical manifestations of infection with CCR5Δ32 mutation was reported [18]. The plausibility of a susceptibility locus in the oligoadenylate synthetase (OAS) gene cluster was reported [19, 20]. More recently, a candidate gene study examining the OAS gene cluster alone suggested a predisposition to WNV infection [21]. Because exposure to WNV is difficult to control for, we sought to examine genetic susceptibility to complications given documented WNV infection. We hypothesized that the use of coding nonsynonymous markers would result in the discovery of ≥1 genetic variants associated with severe WNV disease that may be directly likely to lead to a causal mechanism. We report an association between the replication factor C1 gene, RFC1, and severe WNV disease in the first phase of association testing. We found no association between CCR5Δ32 mutation and a susceptibility to severe complications to WNV infection. Similarly, we found no association between the OAS gene cluster and severe WNV disease. Our results indicate that RFC1 may potentially play a role in the etiology of severe WNV illness.

PARTICIPANTS AND METHODS

Study Participants

During the period September 2004 through December 2009, we enrolled participants from states and provinces in the United States (Nebraska, Pennsylvania, Texas, and Colorado) and Canada (Alberta and Saskatchewan) that were among those with the highest number of individuals with severe WNV disease. Individuals were eligible if they were aged ≥18 years and had evidence of WNV infection on the basis of Centers for Disease Control and Prevention diagnostic laboratory criteria [22]. Case patients were defined as patients who met criteria for WNV infection along with modified case definitions for meningitis, encephalitis, or acute flaccid paralysis [9]. Control participants were individuals who met criteria for infection but who did not meet clinical case definitions for meningitis or encephalitis. Because the majority of testing for WNV is performed centrally by state or provincial laboratories, the state or provincial health departments contacted persons with laboratory-confirmed WNV infection by letter and by telephone to see if they were willing to be approached about the study. Clinical information about WNV infection was obtained by contacting patients’ physicians and through review of medical records. The study received institutional review board approval from McMaster University, McGill University, Nebraska Medical Center, University of Pennsylvania, and University of Texas at San Antonio.

Genotyping

Blood samples were collected at the time of enrollment and genomic DNA obtained with the use of a DNA isolation kit (Qiagen). For the first stage of the study, genotyping was conducted using the Illumina HumanNS-12 BeadChip, based largely on single-nucleotide polymorphisms (SNPs) selected by the Wellcome Trust Case Control Consortium that included all known nsSNPs with >1% minor allele frequency in European populations at the time of study design [23]. The Illumina HumanNS-12 chip included 13 371 SNPs, of which the majority were nonsynonymous variants but also included synonymous, untranslated, and tagSNPs. For the validation and replication stage, 2 panels of 34 SNPs each were designed using Sequenom MassARRAY IPLEX Gold; these included 47 SNPs with the highest association in the derivation cohort at 1 of 2 time points; 15 tagSNPs to fine map the gene that showed the highest association in the derivation cohort; 5 coding nonsynonymous SNPs uncovered in genes that were sequenced; and CCR5Δ32 (rs333), a 32–base pair (bp) deletion in CCR5 that was shown to be associated with clinical symptoms of WNV infection [18]. Duplicate samples were included, and laboratory technicians were blind to the case status of samples.

Sample Quality Control

Illumina HumanNS-12 Array.

There were 1677 unique samples that were genotyped. Twenty samples were present in duplicate, and 1 was in triplicate. Four Centre d'Etude du Polymorphisme Humain controls were genotyped multiple times. Three duplicate samples that did not correspond to the same DNA were removed from further analysis. From the remaining samples, we calculated a pairwise reproducibility rate of 99.9926% before applying any filters on the markers. We computed the mean number of alleles shared identical by state in all possible pairs of individuals to detect population structure and cryptic relatedness in the sample. The set of SNPs used to compute this distribution was restricted to the autosomes and was selected in such a way that no 2 SNPs were allowed to be in linkage disequilibrium with each at r2 > 0.2, in any window of 50 consecutive SNPs. To minimize admixture and other population effects, we restricted the genetic studies to samples of Caucasian origin. We excluded samples obtained from individuals who were not of self-declared non-Hispanic white ancestry, and we conducted a multidimentional scaling analysis comparing samples from the present study to HapMap samples derived from 3 populations [24] in order to identify samples from individuals whose ancestry was likely of non-Caucasian origin. Pairs of individuals who were identified as being likely related were broken up: we kept the case patient if both members of the pair were phenotypically discordant; otherwise, we kept the first participant enrolled. Seemingly related pairs involving 2 participants from distinct collection centers were discarded, on the basis that this was caused by either a sample switch or a contamination. Using this strategy, 29 individuals (17 control patients, 3 case-control pairs due to switches, and 3 case-control pairs with shared DNA) were excluded. Using all the SNPs on the X chromosome, we found inconsistencies regarding the sex of 6 individuals, who were excluded from the analysis. Samples that did not achieve a 90% call rate were excluded from the analysis.

Sequenom Assays.

In a second stage, 2 panels of 34 SNPs were each genotyped in a total of 2350 unique DNA samples, a number that includes the 1677 samples genotyped on the Illumina HumanNS-12 array for the primary analysis. The samples that were removed in the primary analysis (except those that were removed for reason of low call rate) were also removed in the second stage. Samples that were not obtained from participants of self-declared non-Hispanic white ancestry were excluded. Samples that did not reach an 85% call rate in 1 panel were excluded from the analysis of that panel.

SNP Quality Control

To be included in the analysis, an SNP had to have a call rate ≥95%. Genotypic frequencies had to be consistent with the rules of Hardy-Weinberg equilibrium among control patients, at a significance level above a threshold that was chosen so that the distribution of Hardy-Weinberg significance levels among the remaining SNPs did not deviate from what was expected by chance alone (P > .0005 for the Illumina HumanNS-12 assays; P > .001 for the Sequenom assays).

Association Analysis

We used logistic regression to test for association between single SNPs and case-control status, assuming a log-additive effect of the alleles on the risk. For SNPs on the X chromosome, males were treated as homozygous females [25]. We adjusted for the collection center (ie, used as covariate) to account for possible local variations in allele frequencies or local selection. SNPs were deemed to be significant if their P values were below the 5% level, subject to a Bonferroni correction for the number of SNPs tested (P < 4.7 × 10−6, accounting for 10 625 tests). Power to detect an association at this significance level was calculated using CaTS [26]. Forty-eight samples were randomly selected among the cases for resequencing with a view to fine-mapping the RFC1, SCN1A, and ANPEP exons and promoter regions. Primers were designed for amplification of the coding regions, as well as 1kb regions upstream and downstream of the genes.

RESULTS

Nonsynonymous SNP Testing of WNV Severe Disease

We initially genotyped the Illumina HumanNS-12 array in 1677 participants: 608 case patients with neuroinvasive disease (112 meningitis, 72 encephalitis, 195 meningo-encephalitis, and 229 acute flaccid paralysis), 994 control patients, and 75 of equivocal or unknown status. There were 63 samples excluded (44 case patients, 16 control patients, and 3 of equivocal or unknown status) because of non-European ancestry (Supplementary figure 1), 26 samples excluded (2 case patients, 21 control patients, and 1 of unknown status) because of cryptic relatedness, 2 samples (1 case patient and 1 of unknown status) removed because of low call rates (<95%), 6 samples (1 case patient and 5 control patients) removed for inconsistent sex information, and 3 samples genotyped in duplicate (3 control patients) but that corresponded to different DNAs. There were 307 SNPs excluded from analysis for not reaching a sufficient call rate; 52 SNPs failed Hardy-Weinberg equilibrium at a level P < .0005; and 2387 SNPs with minor allele frequency less than 1% (including 1246 monomorphic SNPs) were excluded. The first-stage analysis was performed on 10 625 SNPs in 560 case patients and 950 control patients. A summary of their characteristics is shown in Table 1.
Table 1.

Characteristics of Study Participants

CharacteristicStudy Center
AlbertaColoradoManitobaNebraskaOhioOntarioPennsylvaniaSaskatchewanTexasTotal
Primary samples
    Control patients757303570364134028950
        Female sex, %53.367.15158.36163.257.157.7
        Mean age, years (SD)49.6 (12.5)50.8 (11)54.6 (13.3)52.7 (11.2)54.7 (13.3)52.5 (13.6)52.5 (14.1)53 (13.2)
    Case patients173851990258131164560
        Acute flaccid paralysis341483322116209
        Encephalitis680337301168
        Meningitis35041328157102
        Meningo-Encephalitis52147712181430181
        Female sex, %23.542.140.052.352.051.964.042.048.2
        Mean age, years (SD)56.8 (13.8)56.6 (13)60.2 (12.9)55.6 (15.2)54.7 (14.7)62.9 (15.1)59.4 (14.5)61.3 (14.8)58.7(15)
Replication samples
    Control patients32401826021060296
        Female sex, %66.766.757.766.747.666.759.8
        Mean age, years (SD)54.7 (1.53)54.3 (11.6)54.8 (13.8)58.3 (11.3)55.2 (13.9)56.6 (12.2)55.2 (13.1)
    Case patients020036170101090264
        Acute flaccid paralysis131134152120
        Encephalitis0705214
        Meningitis3125251358
        Meningo-Encephalitis469302372
        Female sex, %4552.864.749.545.649.2
        Mean age, years (SD)59.2 (14.3)56.9 (17.1)70 (15.7)64.6 (15.0)61.5 (13.8)62.4 (15.1)

Abbreviation: SD, standard deviation.

Characteristics of Study Participants Abbreviation: SD, standard deviation. The results from this analysis are summarized in Table 2 for all SNPs selected to undergo replication (see below). The 3 results with the highest levels of significance were from genes potentially biologically relevant: rs2066786 (P = 1.88 × 10−5; odds ratio [OR], 0.68 for the major allele [95% confidence interval {CI}, .57–.81]), an exonic synonomous SNP (Pro847Pro) found in the replication factor C1 gene (RFC1 [MIM 102579]); rs2298771 (P = 5.87 × 10−5; OR, 1.47 [95% CI, 1.22–1.77]), a missense SNP (Ala1056Thr) in the sodium channel, voltage-gated, type I, α subunit gene (SCN1A [MIM 182389]); and rs25651 (P = 1.44 × 10−4; OR, 0.69 [95% CI, .56–.83]), a missense SNP (Ser752Asn) found in the alanyl (membrane) aminopeptidase gene (ANPEP [MIM 151530]). As described in detail below, each of these genes has biological plausibility, with RFC1 potentially having a role in viral replication [27], SCN1A having a role in seizure disorders [28], and ANPEP a receptor for human coronavirus [29].
Table 2.

Results From the Primary Association Analyses Between Single-Nucleotide Polymorphisms (SNPs) and Risk of Neuroinvasive Disease in Patients Infected With the West Nile Virus

SNPRankChromosomePositionAllele
Frequency
HWEOR (95% CI) P
12CasesControls
rs2066786a1438978423TC0.38690.46390.29520.6781 (.5675–.8101).0000188
rs2298771a22166601033CT0.35710.30140.16591.469 (1.218–1.772).0000587
rs25651a31588136791TC0.28570.34230.0039260.685 (.5636–.8326).0001444
rs11575302b5750575187AG0.019640.0110513.825 (1.867–7.838).0002469
rs2177336b83197001272TC0.14640.19890.12780.6659 (.5282–.8395).0005802
rs7163367a101588061148AT0.39640.45840.74430.7349 (.6154–.8777).0006718
rs10839601b12116696711AG0.18210.14840.60711.457 (1.163–1.824).001052
rs3738573a17184636843CG0.32220.3680.52950.7398 (.6170–.8869).001129
rs3733890b18578457714AG0.26310.31930.54880.7293 (.6024–.8830).001214
rs323347a19830825765GA0.15710.19740.25960.6853 (.5438–.8637).001363
rs11652709c221753626092CG0.35270.29790.074281.335 (1.118–1.595).00145
rs1805073b23578362505CG0.25620.30470.39950.7346 (.6071–.8889).001519
rs2824721b252118591988GA0.20710.24840.029880.7123 (.5767–.8797).001638
rs1058587b261918360421GC0.28160.24050.18221.368 (1.126–1.664).001657
rs10778292a2712102784416CT0.11790.15260.25760.6536 (.5010–.8527).001722
rs5370b29612404240TG0.24290.19790.30861.387 (1.127–1.707).002003
rs3816988a301560898791CT0.19860.15750.26851.423 (1.136–1.783).002161
rs12371985b321293466790AG0.015180.0331610.3779 (.2021–.7064).002295
rs2288777b335150900300GA0.054460.074740.64190.5692 (.3958–.8186).002369
rs794999b34312204014AG0.28390.24790.66351.351 (1.113–1.640).002371
rs16826069b35139569641GA0.2580.210.55731.369 (1.115–1.680).002647
rs2278428b361960109865CA0.060710.081050.27010.5904 (.4183–.8333).002725
rs2161468b38199949270GC0.44820.38680.6321.301 (1.093–1.548).003051
rs2270962a3910102006033TC0.057140.075260.64620.5893 (.4152–.8363).003071
rs2270915b40532822145GA0.23920.19860.26191.365 (1.111–1.678).003095
rs4432013b41114687755GA0.19110.21740.6340.7146 (.5718–.8931).003141
rs1527014b421216288943GA0.038390.027890.032822.024 (1.267–3.235).0032
rs13095016b433196999988CG0.14370.18110.82720.6977 (.5492–.8865).003223
rs11079339b441753625440GA0.16070.13260.15651.423 (1.124–1.803).003431
rs2236358b471226002384AG0.054460.0378911.836 (1.219–2.764).00361
rs6263b48750563393CT0.010710.021580.35660.3336 (.1592–.6989).003623
rs2523421b51629498418GA0.11460.092590.077211.496 (1.136–1.969).004082
rs11076256a531657309966TC0.097320.067890.7951.585 (1.155–2.174).004313
rs1128349b54772735589TC0.43040.47160.36250.7757 (.6515–.9235).004326
rs7574414b552227952148AG0.15180.12890.47321.426 (1.116–1.822).004548
rs2287939b57534034639AG0.25710.30110.3160.7519 (.6172–.9158).004606
rs2290182b591665171508CT0.096430.12790.2430.6592 (.4931–.8813).004909
rs7143633b611420619732GC0.16160.13280.67371.418 (1.111–1.810).00505
rs3754274b63184640197AG0.2710.302710.7602 (.6275–.9211).005131
rs7591849b642159821126GT0.4670.41680.94681.276 (1.076–1.514).00514
rs11540407b651274186648TC0.23080.260.40030.7472 (.6088–.9171).005299
rs9380006a66627764477CA0.14820.17470.17730.7209 (.5711–.9099).005871
rs1047881b751211097780CT0.47680.42890.38961.271 (1.068–1.513).006946
rs2241988a89357517212TC0.47860.41840.59411.261 (1.061–1.499).008448
rs6046a13213112821159AG0.10270.12210.87950.708 (.5396–.9291).01275
rs2240154a13519954171TC0.17590.15530.90191.337 (1.063–1.681).01302
rs5748648a2592215660821AG0.042860.066320.5990.6385 (.4315–.9450).02491

SNPs were selected to undergo replication as detailed below. Abbreviations: A, adenine; C, cytosine; CI, confidence interval; G, guanine; HWE, Hardy-Weinberg equilibrium; OR, odds ratio; T, thymine.

Selected for replication according to results from 445 case patients and 813 control patients.

Selected for replication according to results from the complete sample of case patients and control patients.

Selected for replication according to results from the complete sample of case patients and control patients but replaced with rs10853004 in the replication panels. Frequency estimates and ORs (along with their 95% CIs) are calculated with respect to allele 1. Also included are significance levels for tests of HWE.

Results From the Primary Association Analyses Between Single-Nucleotide Polymorphisms (SNPs) and Risk of Neuroinvasive Disease in Patients Infected With the West Nile Virus SNPs were selected to undergo replication as detailed below. Abbreviations: A, adenine; C, cytosine; CI, confidence interval; G, guanine; HWE, Hardy-Weinberg equilibrium; OR, odds ratio; T, thymine. Selected for replication according to results from 445 case patients and 813 control patients. Selected for replication according to results from the complete sample of case patients and control patients. Selected for replication according to results from the complete sample of case patients and control patients but replaced with rs10853004 in the replication panels. Frequency estimates and ORs (along with their 95% CIs) are calculated with respect to allele 1. Also included are significance levels for tests of HWE.

Novel Coding Nonsynonymous Variants in RFC1, SCN1A, and ANPEP Regions

The 3 genes were sequenced (exons and promoter regions only) in a set of 48 randomly chosen cases (totaling 96 chromosomes), in order to find potential novel candidate risk factors. A total of 5 coding nonsynonymous variants not already genotyped were uncovered in the 3 genes (1 in RFC1, 2 in SCN1A, and 2 in ANPEP). All these novel variants showed at most 2 instances of the nonreference allele out of the 96 chromosomes, making them rare variants, each with estimated frequency <2% in the case patients. In addition, we confirmed a number of known and rare coding nonsynonymous SNPs that were already indexed in the dbSNP database at the time of the sequencing experiment.

Validation of Significant Markers

To validate findings from the first stage of the analysis, we genotyped 68 SNPs, including 47 SNPs taken on the basis of the results of the analysis of the Illumina HumanNS-12 array, at 2 different time points during the course of the project (the top 15 SNPs on the basis of a preliminary analysis of the first 445 case patients and 813 control patients collected, and the top 32 SNPs not already selected on the basis of the analysis of the complete sample, after pruning SNPs found to be in high linkage disequilibrium [r2 > 0.8] with better ranked SNPs); 15 tagSNPs to try to refine the association seen in RFC1 with rs2066786, a variant that does not change the amino acid in the amino acid sequence of the gene; and the 5 coding nonsynonymous SNPs uncovered after sequencing the RFC1, SCN1A, and ANPEP genes. In addition, we included in a panel rs333, a 32-bp deletion in CCR5 shown to be associated with increased risk of WNV infection, for which a design on the Illumina HumanNS-12 array was not available. Two control patients had insufficient call rates on both Sequenom panels and were excluded from the analysis; an additional 25 samples (6 case patients, 16 control patients, and 1 equivocal) failed only 1 Sequenom panel and were excluded only for SNPs on that panel. After excluding samples with low call rates, the SNPs were tested in the original set of (up to, depending on panel failure) 560 case patients and 950 control patients, in an additional set of 560 samples (264 case patients and 296 control patients), and in all samples jointly considered. The results are summarized in Supplementary table 1. For rs2066786 in RFC1,P = .58 in the replication set and a joint P = .0022. For rs2298771 in SCN1A,P = .58 in the replication cohort, with joint P = .0050. For rs25651 in ANPEP, the replication P value is .037, but the risk allele (the one that is more frequent among the case patients) is different from the risk allele in the original sample set, and as a result the joint P value increases to .042. None of the following 44 ranked SNPs replicated in the additional samples genotyped. Fine mapping of RFC1 did not provide additional insights: significance levels are comparable to the original significance observed for rs2066786 and can be explained by the linkage disequilibrium structure in the region (Supplementary figure 2).

Effect of Rare Coding Nonsynonymous Variants

The rare coding nonsynonymous variants uncovered by sequencing RFC1, SCN1A, and ANPEP were genotyped. No significant associations (Fisher exact test) were found between these rare mutations and the case/control status (data not shown). Collapsing the counts of carriers of rare variants across SNPs within genes did not provide additional insights.

Lack of Replication of Associations Reported in the Literature

An association between symptomatic WNV disease and homozygosity for the CCR5Δ32 mutation in the chemokine receptor gene CCR5 was initially reported [16, 17]. More recently, this association was not replicated, but results suggestive of a link to clinical manifestations of infection with CCR5Δ32 mutation were reported [18]. The plausibility of a susceptibility locus in the OAS gene cluster was reported [19, 20]. More recently, a candidate gene study examining the OAS gene cluster alone suggested a predisposition to WNV infection [21]. There were 13 (1.58%) of 821 case patients who were homozygous for the CCR5Δ32 deletion, compared with 13 (1.05%) of 1233 control patients (OR, 1.51 [95% CI, .70–3.27]; P = .30). For genes in the OAS cluster, ORs ranged from 1.1 to 1.13 for 4 OAS variants with all P values >.05 (Supplementary table 2).

Power to Detect an Association

We calculated the power that a sample consisting of 824 case patients and 1246 control patients (the sample used in the joint analysis) has to detect an association with a polymorphism whose risk is assumed to act additively with the number of risk alleles an individual possesses assuming homogeneity across collection centers. Supplementary figure 3 shows that the sample has 80% power to detect an association at P < 4.7 × 10−6 (a level deemed to be significant even after a Bonferroni correction for all SNPs tested) for relative risks as low as 1.5 (depending on the risk allele frequency).

DISCUSSION

We sought to test for common genetic polymorphisms associated with severe WNV complications in a North American population using mainly nonsynonomous SNPs. We identified a variant in each of 3 genes (RFC1, SCN1A, ANPEP) potentially implicated in susceptibility to such neurological complications. Joint analysis showed for RFC1-rs2066786, P = .0022 SCN1A-rs2298771, P = .005 and ANPEP-rs25651, P= .042. SNPs in other candidate genes (OAS, TLR3 [Toll-like receptor 3 gene]) were not significant. Our findings reveal preliminary information that genetic variants may play a role in susceptibility to severe WNV disease. Human replication factor C (RFC), also called activator-1, is a multimeric primer-recognition protein consisting of 5 distinct subunits [27]. Human RFC was purified from extracts of HeLa cells as a host factor essential for the in vitro replication of simian virus 40 DNA. RFC allows for efficient elongation of DNA in the presence of human single-stranded DNA binding protein. Using interaction cloning, Uchiumi et al found that the large subunit of RFC interacts with the DNA sequence repeats of telomeres [30]. They found that RFC recognizes the 5′-phosphate termini of double-stranded telomeric repeats. The authors suggested that RFC may be involved in telomere stability or turnover. Presumably, if RFC1 plays a pathogenic role in WNV, it could be with respect to facilitating viral replication. To explain the lack of replication of rs2066786, we assessed the effect of this variant on the 3 subcategories of neuroinvasive disease (meningitis, encephalitis, or acute flaccid paralysis). As shown in Table 2, there was no difference in effects. Population structure in the replication cohort is another possible explanation for lack of replication. Indeed, continental differences in European ancestry have been described [31]. However, we did not identify any differences even adjusting for ancestry informative markers. We reviewed phenotype data and could not identify any systematic differences in phenotyping. An association between the 32-bp deletion in CCR5 and West Nile clinical symptoms has been reported [18]. We found no evidence of any effect. One possible reason for the discrepancy is that we compared case patients with control patients, all of whom were symptomatic when infected with WNV. In contrast, the published reports compared case patients with control patients with no symptoms. Notably, we found no association between any of the OAS polymorphisms and WNV. We believe that this highlights the importance of conducting human population-based studies. Although TLR was also reported to be a mechanism in a murine model, we saw no significant effect in this population-based study [32]. Strengths of this study include the rigorous definitions and procedures used to confirm case and control status. All clinical and laboratory data had to have supportive documentation, including laboratory records and clinical information. Selection of case patients and control patients was as unbiased as could be achieved. In the states and provinces that we selected, all of those who were infected were invited to participate through notification from public health laboratories where the great majority of testing occurred. We acknowledge that sample size for such an association study was limited, but this is a function of the number of WNV neuroinvasive cases seen in North America.

Funding

This work was supported by the National Institute of Allergy and Infectious Diseases, National Institutes of Health (contract HHSN266200400066C).
  31 in total

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Journal:  Eur J Hum Genet       Date:  2008-12       Impact factor: 4.246

3.  CCR5 deficiency is a risk factor for early clinical manifestations of West Nile virus infection but not for viral transmission.

Authors:  Jean K Lim; David H McDermott; Andrea Lisco; Gregory A Foster; David Krysztof; Dean Follmann; Susan L Stramer; Philip M Murphy
Journal:  J Infect Dis       Date:  2010-01-15       Impact factor: 5.226

4.  Variation in antiviral 2',5'-oligoadenylate synthetase (2'5'AS) enzyme activity is controlled by a single-nucleotide polymorphism at a splice-acceptor site in the OAS1 gene.

Authors:  Vagn Bonnevie-Nielsen; L Leigh Field; Shao Lu; Dong-Jun Zheng; Min Li; Pia M Martensen; Thomas B Nielsen; Henning Beck-Nielsen; Yu-Lung Lau; Flemming Pociot
Journal:  Am J Hum Genet       Date:  2005-02-24       Impact factor: 11.025

5.  A second locus for familial generalized epilepsy with febrile seizures plus maps to chromosome 2q21-q33.

Authors:  S Baulac; I Gourfinkel-An; F Picard; M Rosenberg-Bourgin; J F Prud'homme; M Baulac; A Brice; E LeGuern
Journal:  Am J Hum Genet       Date:  1999-10       Impact factor: 11.025

Review 6.  The continuing spread of West Nile virus in the western hemisphere.

Authors:  Duane J Gubler
Journal:  Clin Infect Dis       Date:  2007-09-14       Impact factor: 9.079

7.  A second generation human haplotype map of over 3.1 million SNPs.

Authors:  Kelly A Frazer; Dennis G Ballinger; David R Cox; David A Hinds; Laura L Stuve; Richard A Gibbs; John W Belmont; Andrew Boudreau; Paul Hardenbol; Suzanne M Leal; Shiran Pasternak; David A Wheeler; Thomas D Willis; Fuli Yu; Huanming Yang; Changqing Zeng; Yang Gao; Haoran Hu; Weitao Hu; Chaohua Li; Wei Lin; Siqi Liu; Hao Pan; Xiaoli Tang; Jian Wang; Wei Wang; Jun Yu; Bo Zhang; Qingrun Zhang; Hongbin Zhao; Hui Zhao; Jun Zhou; Stacey B Gabriel; Rachel Barry; Brendan Blumenstiel; Amy Camargo; Matthew Defelice; Maura Faggart; Mary Goyette; Supriya Gupta; Jamie Moore; Huy Nguyen; Robert C Onofrio; Melissa Parkin; Jessica Roy; Erich Stahl; Ellen Winchester; Liuda Ziaugra; David Altshuler; Yan Shen; Zhijian Yao; Wei Huang; Xun Chu; Yungang He; Li Jin; Yangfan Liu; Yayun Shen; Weiwei Sun; Haifeng Wang; Yi Wang; Ying Wang; Xiaoyan Xiong; Liang Xu; Mary M Y Waye; Stephen K W Tsui; Hong Xue; J Tze-Fei Wong; Luana M Galver; Jian-Bing Fan; Kevin Gunderson; Sarah S Murray; Arnold R Oliphant; Mark S Chee; Alexandre Montpetit; Fanny Chagnon; Vincent Ferretti; Martin Leboeuf; Jean-François Olivier; Michael S Phillips; Stéphanie Roumy; Clémentine Sallée; Andrei Verner; Thomas J Hudson; Pui-Yan Kwok; Dongmei Cai; Daniel C Koboldt; Raymond D Miller; Ludmila Pawlikowska; Patricia Taillon-Miller; Ming Xiao; Lap-Chee Tsui; William Mak; You Qiang Song; Paul K H Tam; Yusuke Nakamura; Takahisa Kawaguchi; Takuya Kitamoto; Takashi Morizono; Atsushi Nagashima; Yozo Ohnishi; Akihiro Sekine; Toshihiro Tanaka; Tatsuhiko Tsunoda; Panos Deloukas; Christine P Bird; Marcos Delgado; Emmanouil T Dermitzakis; Rhian Gwilliam; Sarah Hunt; Jonathan Morrison; Don Powell; Barbara E Stranger; Pamela Whittaker; David R Bentley; Mark J Daly; Paul I W de Bakker; Jeff Barrett; Yves R Chretien; Julian Maller; Steve McCarroll; Nick Patterson; Itsik Pe'er; Alkes Price; Shaun Purcell; Daniel J Richter; Pardis Sabeti; Richa Saxena; Stephen F Schaffner; Pak C Sham; Patrick Varilly; David Altshuler; Lincoln D Stein; Lalitha Krishnan; Albert Vernon Smith; Marcela K Tello-Ruiz; Gudmundur A Thorisson; Aravinda Chakravarti; Peter E Chen; David J Cutler; Carl S Kashuk; Shin Lin; Gonçalo R Abecasis; Weihua Guan; Yun Li; Heather M Munro; Zhaohui Steve Qin; Daryl J Thomas; Gilean McVean; Adam Auton; Leonardo Bottolo; Niall Cardin; Susana Eyheramendy; Colin Freeman; Jonathan Marchini; Simon Myers; Chris Spencer; Matthew Stephens; Peter Donnelly; Lon R Cardon; Geraldine Clarke; David M Evans; Andrew P Morris; Bruce S Weir; Tatsuhiko Tsunoda; James C Mullikin; Stephen T Sherry; Michael Feolo; Andrew Skol; Houcan Zhang; Changqing Zeng; Hui Zhao; Ichiro Matsuda; Yoshimitsu Fukushima; Darryl R Macer; Eiko Suda; Charles N Rotimi; Clement A Adebamowo; Ike Ajayi; Toyin Aniagwu; Patricia A Marshall; Chibuzor Nkwodimmah; Charmaine D M Royal; Mark F Leppert; Missy Dixon; Andy Peiffer; Renzong Qiu; Alastair Kent; Kazuto Kato; Norio Niikawa; Isaac F Adewole; Bartha M Knoppers; Morris W Foster; Ellen Wright Clayton; Jessica Watkin; Richard A Gibbs; John W Belmont; Donna Muzny; Lynne Nazareth; Erica Sodergren; George M Weinstock; David A Wheeler; Imtaz Yakub; Stacey B Gabriel; Robert C Onofrio; Daniel J Richter; Liuda Ziaugra; Bruce W Birren; Mark J Daly; David Altshuler; Richard K Wilson; Lucinda L Fulton; Jane Rogers; John Burton; Nigel P Carter; Christopher M Clee; Mark Griffiths; Matthew C Jones; Kirsten McLay; Robert W Plumb; Mark T Ross; Sarah K Sims; David L Willey; Zhu Chen; Hua Han; Le Kang; Martin Godbout; John C Wallenburg; Paul L'Archevêque; Guy Bellemare; Koji Saeki; Hongguang Wang; Daochang An; Hongbo Fu; Qing Li; Zhen Wang; Renwu Wang; Arthur L Holden; Lisa D Brooks; Jean E McEwen; Mark S Guyer; Vivian Ota Wang; Jane L Peterson; Michael Shi; Jack Spiegel; Lawrence M Sung; Lynn F Zacharia; Francis S Collins; Karen Kennedy; Ruth Jamieson; John Stewart
Journal:  Nature       Date:  2007-10-18       Impact factor: 49.962

8.  The epidemic of West Nile virus in the United States, 2002.

Authors:  Daniel R O'Leary; Anthony A Marfin; Susan P Montgomery; Aaron M Kipp; Jennifer A Lehman; Brad J Biggerstaff; Veronica L Elko; Peggy D Collins; John E Jones; Grant L Campbell
Journal:  Vector Borne Zoonotic Dis       Date:  2004       Impact factor: 2.133

Review 9.  West Nile virus infections.

Authors:  Kymberly A Gyure
Journal:  J Neuropathol Exp Neurol       Date:  2009-10       Impact factor: 3.685

10.  Sex chromosomes and genetic association studies.

Authors:  David G Clayton
Journal:  Genome Med       Date:  2009-11-24       Impact factor: 11.117

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  20 in total

Review 1.  The role of chemokines in the pathogenesis of neurotropic flaviviruses.

Authors:  Susana V Bardina; Jean K Lim
Journal:  Immunol Res       Date:  2012-12       Impact factor: 2.829

2.  Meta-analysis of genetic association studies under heterogeneity.

Authors:  Binod Neupane; Mark Loeb; Sonia S Anand; Joseph Beyene
Journal:  Eur J Hum Genet       Date:  2012-05-30       Impact factor: 4.246

Review 3.  Risk factors for West Nile virus infection and disease in populations and individuals.

Authors:  Ruth R Montgomery; Kristy O Murray
Journal:  Expert Rev Anti Infect Ther       Date:  2015-01-30       Impact factor: 5.091

4.  Human Immunodeficiency Virus (HIV).

Authors: 
Journal:  Transfus Med Hemother       Date:  2016-05-09       Impact factor: 3.747

5.  Nucleic acid sensing and innate immunity: signaling pathways controlling viral pathogenesis and autoimmunity.

Authors:  Laura R H Ahlers; Alan G Goodman
Journal:  Curr Clin Microbiol Rep       Date:  2016-06-29

6.  Of Mice and Men: Protective and Pathogenic Immune Responses to West Nile virus Infection.

Authors:  Derek Trobaugh; Sharone Green
Journal:  Curr Trop Med Rep       Date:  2015-03-01

Review 7.  The Role of Nucleic Acid Sensing in Controlling Microbial and Autoimmune Disorders.

Authors:  Keesha M Matz; R Marena Guzman; Alan G Goodman
Journal:  Int Rev Cell Mol Biol       Date:  2018-09-25       Impact factor: 6.813

Review 8.  West Nile Virus: biology, transmission, and human infection.

Authors:  Tonya M Colpitts; Michael J Conway; Ruth R Montgomery; Erol Fikrig
Journal:  Clin Microbiol Rev       Date:  2012-10       Impact factor: 26.132

Review 9.  Age-related alterations in immune responses to West Nile virus infection.

Authors:  R R Montgomery
Journal:  Clin Exp Immunol       Date:  2016-10-17       Impact factor: 4.330

10.  Association between high expression macrophage migration inhibitory factor (MIF) alleles and West Nile virus encephalitis.

Authors:  Rituparna Das; Kerry Loughran; Charles Murchison; Feng Qian; Lin Leng; Yan Song; Ruth R Montgomery; Mark Loeb; Richard Bucala
Journal:  Cytokine       Date:  2015-11-28       Impact factor: 3.861

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