Genetic variation in the HLA region is associated with susceptibility to herpes zoster

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Abstract

Herpes zoster, commonly referred to as shingles, is caused by the varicella zoster virus (VZV). VZV initially manifests as chicken pox, most commonly in childhood, can remain asymptomatically latent in nerve tissues for many years and often re-emerges as shingles. Although reactivation may be related to immune suppression, aging and female sex, most inter-individual variability in re-emergence risk has not been explained to date. We performed a genome-wide association analyses in 22 981 participants (2280 shingles cases) from the electronic Medical Records and Genomics Network. Using Cox survival and logistic regression, we identified a genomic region in the combined and European ancestry groups that has an age of onset effect reaching genome-wide significance ( P>1.0 × 10 ⁻⁸). This region tags the non-coding gene HCP5 (HLA Complex P5) in the major histocompatibility complex. This gene is an endogenous retrovirus and likely influences viral activity through regulatory functions. Variants in this genetic region are known to be associated with delay in development of AIDS in people infected by HIV. Our study provides further suggestion that this region may have a critical role in viral suppression and could potentially harbor a clinically actionable variant for the shingles vaccine.

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Common Genetic Variation and the Control of HIV-1 in Humans

Jacques Fellay, Dongliang Ge, Kevin V. Shianna … (2009)

Introduction The clinical outcome of HIV-1 infection is highly variable and determined by complex interactions between virus, host and environment. Several human genetic factors have been reported to modulate HIV-1 disease [1],[2], but current knowledge only explains a small fraction of the observed variability in the course of infection. Our first genome-wide association study (GWAS) of human genetic variants that associate with HIV-1 control analyzed 486 individuals of European ancestry and identified two genome-wide significant determinants of viremia at set point, and one determinant of disease progression [3]. The 3 single nucleotide polymorphisms (SNPs) collectively explained 14% of the variation in viral load at set point and 10% of the variation in disease progression. All 3 were located in the Major Histocompatibility Complex (MHC) region on chromosome 6, confirming, in a genome-wide context, the essential role played by the MHC region in HIV-1 control [4],[5],[6]. Since then, the findings have been replicated by several independent groups that used either targeted genotyping [7],[8],[9] or whole genome approaches [10],[11]. The power of our initial study was limited to the detection of common variants (with minor allele frequency of 5% or more) which explain a sizable fraction of the phenotypic variability: it had an 80% power of identifying SNPs that explain at least 5% of the variability. Here we have increased the sample size to 2554 which allows us to provide a much more thorough investigation of the role of common variation in the control of HIV-1: considering the final number of participants included in the analysis, the study was powered to detect the effects of common variants down to 1.3% of explained variability. One surprising feature of the first genome-wide investigation was that it failed to identify any of the many non-MHC candidate gene variants that have been reported to associate with HIV-1 disease outcomes over the past 15 years. Candidate gene studies claimed associations for variants in genes selected for their known or suspected role in HIV-1 pathogenesis and in immune response [1],[2] [compiled in http://www.hiv-pharmacogenomics.org]. Collectively, this body of work has implicated various components of innate, adaptive, and intrinsic immunity in HIV-1 control, cellular co-factors important in viral life cycle, and quite unexpected candidates such as the vitamin D receptor. We here use our large sample size and the power of genome-wide data, which allows precise correction for population stratification, to evaluate most candidate gene discoveries. Genome-wide analysis also makes it possible to assess genetic interactions, copy number polymorphisms, enrichment of gene sets and of functional variants: these analyses were pursued in the present work. Collectively, our study circumscribes the impact of common variation in the control of HIV-1 in an adult and predominantly male Caucasian population. Results Subjects A total of 2554 HIV-1 infected individuals of self-reported Caucasian ancestry were genotyped on Illumina whole-genome chips (Table S1). Participants were recruited between 1984 and 2007 in one of the 9 cohorts forming the Euro-CHAVI Consortium (N = 1397, including 486 subjects that were included in our previous study [3], 75.7% male, median age: 33 years) or in the MACS cohort (N = 1157, 100% male, median age: 33 years). A subset of 1113 patients had a proven date of seroconversion (“seroconverters”) and the remainder had a confirmed stable viremia profile but no known date of infection (“seroprevalent” patients). Several quality control steps ( Text S1 ) resulted in the exclusion of 115 individuals with insufficient genotype call rates, of 17 individuals that were found to be genetically related with another study subject, and of 5 individuals with a gender discrepancy between phenotype and genotype data. To control for population stratification, we performed a principal component analysis of the whole-genome genotyping data (Eigenstrat [12], Text S1 ), which identified 12 significant axes after exclusion of 55 outlier subjects. The most significant principal component is a north-to-south European axis that has already been described [13] (Figure S1). A total of 2362 individuals were included in the set point association analyses (including 486 subjects studied before [3]), and 1071 seroconverters were eligible for the analysis of disease progression (including 337 subjects studied before [3]). A subset of 1204 subjects had complete 4-digit HLA Class I results and could be included in models assessing the respective influences of SNPs and HLA alleles on HIV-1 control. Common variants and variation in viral load at set point All QC-passed SNPs (Text S1, Table S11) were tested for association with HIV-1 viremia at set point in separate linear regression models that included gender, age, and the 12 significant PC axes as covariates. The global distribution of resulting p-values was very close to the null expectation (λ = 1.006, Figure S2) indicating that stratification was adequately controlled [14]. Male gender and older age both associated with higher set point (p = 1.9E-21 and p = 2.6E-05, respectively) and explained 4% of the inter-individual variability. The population sub-structure, reflected in the 12 significant PC axes, explained an additional 3.4%. The 2 SNPs previously reported as genome-wide significant [3] were confirmed to be the strongest determinants of variation in HIV-1 viral load (Figure 1 and Figure 2): rs2395029, an HCP5 T>G variant, which is known to be an almost perfect proxy for HLA-B*5701 in Caucasians [15],[16] (p = 4.5E–35), and rs9264942, a T>C SNP located in the 5′ region of HLA-C, 35 kb away from transcription initiation (p = 5.9E–32). Of note, the association signals were also convincingly genome-wide significant in an analysis restricted to the subjects that were not included in our initial study (Table 1). Each SNP explained 5 to 6% of the variability in set point viremia, as determined by the increase in the r2 value of the respective linear regression models: the effect size estimates are smaller in the expanded data set than in the previously reported study. This is due, at least in part, to the inclusion of seroprevalent subjects with less stringent phenotype definition. Interestingly, we observed a break in the strong linkage disequilibrium (LD) between HCP5 and HLA-B in 9/1204 subjects with HLA Class I results (0.7%, r2 = 0.93): the set point values were lower for the 4 patients that had B*5701 without the rs2395029 minor allele than for the 5 patients with a G at rs2395029 but without B*5701 (median [IQR] log10 HIV-1 cp/ml = 3.14 [2.77–3.68] vs. 4.22 [4.14–4.28] respectively (Table S2); p = 0.05, Kruskal-Wallis rank test). Consequently, the association with set point was stronger for B*5701 (p = 3.8E–19) than it was for rs2395029 (p = 1.7E–17) in the same subset of patients. 10.1371/journal.pgen.1000791.g001 Figure 1 Significant hits in the MHC region. Representation of a 3 Mb stretch in the MHC region, encompassing the HLA Class I gene loci and the genome-wide significant SNPs identified in the study (red dots represents SNPs with p-value G; H65R, which is predicted to be a high-risk change by FastSNP [17] (non-conservative amino acid change; possibly splicing regulation); [3] rs9266409, located in the 3′ region of HLA-B, 12 kb away; [4] rs8192591, a non-synonymous coding SNP located in the 9th exon of the NOTCH4 gene: 1739A>G; S534G (conservative amino acid change; possibly influencing splicing). We emphasize that these analyses demonstrate that there are further independent effects in the MHC region, but they do not prove that the specific SNPs implicated are responsible for those effects. Some or all of these SNPs are most likely markers for one or more variants that have not been genotyped and which provide aspects of viral control independent of the two previously reported associations. As one possible contribution to these associations we assessed the HLA Class I alleles in the subset of 1204 subjects who had full MHC typing results. The 4-digit alleles were tested separately and in models including the identified MHC SNPs (Table 2, Table S4). 15 alleles were found to associate with set point, but only 4 of them (A*3201, B*1302, B*2705 and B*3502) had an independent effect that was still detectable in models including the top associated SNPs. Several HLA-C alleles associated significantly with viral control before but not after adjustment for the top SNPs (Table 2, Table S6). This is partly because all HLA-C alleles are in LD with the HLA-C -35 rs9264942. They can in fact be perfectly divided into 2 mutually exclusive groups on the basis of their LD with the rs9264942 C or T allele. The C-related alleles, as a group, strongly associated with lower setpoint (p = 2.8E–14) but failed to entirely recapitulate the rs9264942 association signal (p = 8.4E–16 in this group). Homozygosity for the HLA Class I loci also showed a weak independent association (p = 0.03 after adjustment for the SNPs). Altogether, a model including 6 SNPs, 4 alleles and homozygosity status shows that MHC variation explains 12% of the set point variability in this cohort. 10.1371/journal.pgen.1000791.t002 Table 2 Associations between 4-digit HLA Class I alleles and HIV-1 set point in a subset of 1,204 subjects with full results. HLA Allele Model with HLA allele only Model with HLA allele, rs2395029 Model with HLA allele, rs2395029, rs9264942 Model with HLA allele, rs2395029, rs9264942, rs259919, rs9468692, rs9266409, rs8192591 Effect A*2402 3.4E–02 6.9E–02 1.1E–01 9.9E–01 A*2501 2.8E–02 2.7E–02 1.2E–01 3.2E–01 A*3201 5.0E–03 1.0E–03 6.0E–03 2.9E–02 protective B*0702 7.0E–03 1.5E–02 4.3E–01 3.8E–01 B*0801 7.0E–03 3.3E–02 5.8E–01 2.4E–01 B*1302 2.0E–03 2.1E–04 1.2E–02 1.6E–02 protective B*2705 5.2E–05 4.9E–06 2.0E–03 3.0E–03 protective B*3502 1.9E–05 3.9E–05 6.2E–04 2.0E–03 deleterious B*5601 4.2E–02 7.8E–02 1.9E–01 1.2E–01 C*0202 1.0E–03 2.5E–04 6.0E–02 5.0E–02 C*0401 5.0E–03 3.0E–02 9.3E–01 8.5E–01 C*0602 1.2E–11 4.9E–02 7.1E–01 7.8E–01 C*0701 5.4E–04 3.0E–03 1.8E–01 7.8E–01 C*0702 3.4E–02 6.3E–02 9.9E–01 9.5E–01 C*0802 1.2E–02 2.0E–03 1.6E–01 2.5E–01 P-values are shown for all Class I alleles that have a nominally significant association with HIV-1 viral load at set point (with the exception of B*5701, discussed in the text). All linear regression models include gender, age and the 12 Eigenstrat axes as covariates. Most of the association signals disappear once the top associated SNPs are added. However, A*3201, B*1302, B*2705 and B*3502 still have an independent effect. See Table S4 for a complete list of all HLA Class I allele results. In addition, Table S5 lists all pairs of HLA-B and HLA-C alleles that are in LD (with an r2>0.1) and therefore can represent the same association signal (as for example in the case of HLA-C*0602, which is often on the same haplotype as HLA-B*5701). HIV-1 disease progression We defined HIV-1 disease progression as the drop of CD4 T cell count to below 350 cells/ul or the initiation of potent antiretroviral treatment (cART) following a CD4 T cell count 0.8) on all the chips that we used. Two were present on the Human1M chips only and 9 were not represented: these 11 SNPs were genotyped by TaqMan assays. The CCL3L1 copy number polymorphism was also assessed and the absence of any association with HIV-1 control has been recently reported [19]. 10.1371/journal.pgen.1000791.t003 Table 3 Variants in HIV-1 candidate genes, previously reported to associate with viral control or disease progression. group SNP gene variant genotyping proxy r2 model p setpoint N p progression N effect Chemokine receptors rs333 CCR5 delta 32 TaqMan no dominant 1.7E–10 2333 3.5E–07 1054 protective rs1799988 CCR5 P1 - 627T>C TaqMan no recessive 7.5E–05 1791 3.8E–04 1012 deleterious rs1799864 CCR2 V64I TaqMan no dominant 8.1E–03 2317 1.5E–01 1056 protective rs3732378 CX3CR1 T280M present on all chips - recessive 1.5E–01 2362 6.0E–01 1071 Chemokines rs1719134 CCL3/MIP1a intron 459C>T proxy on all chips rs1634508 1 dominant 6.0E–01 2362 8.2E–01 1071 rs2107538 CCL5/RANTES promoter -403G>A proxy on all chips rs2291299 1 haplotypes R1-R5: additive 2.9E–01 2362 2.3E–01 1069 rs2280788 promoter -28C>G proxy on all chips rs4251739 1 rs2280789 In1.1 T>C proxy on all chips rs2306630 1 rs1801157 SDF-1/CXCL12 SDF-1 3′A proxy on all chips rs10900029 1 recessive 8.6E–01 2362 9.2E–01 1071 Cytokines rs2243250 IL-4 promoter -589C>T proxy on all chips rs2243290 1 additive 3.4E–01 2362 6.3E–01 1065 rs1800872 IL-10 promoter -592C>A proxy on all chips rs3024490 1 dominant 6.5E–02 2362 1.4E–01 1065 rs1799946 DEFB1 promoter -52G>A proxy on all chips rs2741127 0.9 recessive 1.9E–01 1830 2.5E–01 931 Intracellular life cycle rs8177826 PPIA promoter 1604C>G TaqMan no dominant 6.0E–01 1808 6.3E–01 1065 rs6850 promoter 1650A>G TaqMan no dominant 7.7E–01 1753 1.4E–01 1065 rs2292179 TSG101 promoter -183T>C proxy on all chips rs3781640 1 haplotypes 7.9E–01 1792 1.9E–01 929 rs1395319 intron 181A>C TaqMan no Intrisic immunity rs8177832 APOBEC3G NS coding H186R present on all chips - additive 3.2E–01 2362 5.2E–01 1071 rs3740996 TRIM5a NS coding H43Y present on all chips - recessive 7.3E–01 2362 9.3E–01 1071 rs10838525 NS coding R136Q present on all chips - additive 9.0E–01 2362 8.3E–01 1071 Innate immunity rs2287886 DC-SIGN/CD209 promoter -139T>C present on all chips - additive 9.2E–01 2362 1.2E–01 1065 rs5030737 MBL2 NS coding R52C 1M chip + TaqMan no recessive 4.5E–01 1735 2.9E–01 503 rs1800450 NS coding G54D present on all chips - recessive 4.3E–01 2361 9.0E–01 1064 rs1800451 NS coding G57E 1M chip + TaqMan no recessive 5.5E–01 1728 8.5E–01 501 rs352139 TLR9 intron 1174G>A proxy on all chips rs352163 0.9 additive 6.5E–01 2362 7.9E–01 1065 rs352140 syn coding P545P proxy on all chips 0.9 rs3764880 TLR8 NS coding V1M present on all chips - additive 5.0E–01 2361 9.3E–01 1064 Others rs601338 FUT2 W154stop proxy on all chips rs504963 0.8 dominant 1.1E–01 2362 7.0E–02 1065 rs1801274 FCGR2A NS coding H131R present on all chips - recessive 7.9E–01 2360 2.0E–01 1065 rs1544410 VDR intron 8 variant present on all chips - recessive 5.1E–01 2360 6.0E–01 1064 See http://www.hiv-pharmacogenomics.org/pdf/ref_tbl_nat_history/The_complete_reference_table_for_HIV_natural_history_modifiers.pdf for references. Dominant or additive genetic models were used in the analyses for individual SNPs on the basis of their described effect and/or their minor allele frequencies. The P1 variant in the CCR5 promoter region is defined by the SNP rs1799988 (627 C>T). Haplotypes R1 to R5 in the CCL5 (RANTES) gene were defined using 2 promoter variants (−403C>G, defining haplotype R1, and −28C>G, defining haplotype R5), and 1 intronic variant (375T>C, or In1.1, present in haplotypes R3, R4 and R5). The haplotype R4 is defined by a −222T>C SNP that is monomorphic in Caucasians and was therefore absent in our study population. A combined variable was then defined and tested in additive models: 0 = putatively deleterious haplotypes (presence of an R3 haplotype in the absence of R1 and R5), 1 = neutral haplotypes (all other) and 2 = haplotypes putatively protective (presence of an R1 or R5 haplotype in the absence of R3). For the TSG101 gene, 2 SNPs defined haplotype B (−183T/181C), haplotype C (−183C/181C) and haplotype A (−183T/181A). Again, a combined variable was defined and tested in additive models: 0 = haplotypes putatively deleterious (AC or CC), 1 = neutral haplotypes (AA or BC) and 2 = haplotypes putatively protective (AB or BB). Only variants from the chromosome 3 CCR5-CCR2 genomic region showed nominally significant association with the HIV-1-related outcomes under study. SNP: single nucleotide polymorphism. proxy: high-LD SNP (r2>0.8) that can be used as a tag for the original variant. r2: r-squared. p: p-value. The CCR5-Δ32 variant, a 32 bp deletion in the main HIV-1 co-receptor that protects against HIV-1 acquisition when present in homozygous form [20],[21],[22], strongly associated with both set point (p = 1.7E–10) and progression (p = 3.5E–07). CCR5-Δ32 explained 1.7% of the variability in viral control. Only two other variants showed significant associations: The CCR5 promoter variant P1 [23], found on a haplotype known to increase CCR5 expression [24], associated with higher set point and faster progression, whereas the CCR2-64I variant, a Valine to Isoleucine change in the HIV-1 minor receptor CCR2 [25],[26] associated with better viral control (Table 3). They together explained 1% of the variability in viral control. Partial association results for the two CCR5 variants in a subset of our study population were reported elsewhere [19]. Effect of identified genetic determinants throughout the full phenotype range By design, this study focused on the control of HIV-1 as a quantitative trait, with a particular focus on the amount of virus during the set point period. One important question to address therefore is whether the genetic determinants identified influence viral control throughout the full phenotypic range, from those with low to high viral loads, and from slow to fast progression times. To address this, we looked at allele frequencies in individuals that maintained variable degrees of viral control: we found a consistent enrichment of the protective alleles in categories of subjects with good viral control or slow disease progression rate in comparison to subjects with poor control or rapid progression (Figure 4). 10.1371/journal.pgen.1000791.g004 Figure 4 Allelic distribution of the significant variants in subsets of the study population. The bar graphs show the allelic distribution of the 4 variants that have a genome-wide significant association with HIV-1 set point and/or disease progression in subsets of the study population. Groups were defined according to HIV-1 set point (left-hand side graphs) and to progression time (right-hand side graphs). Additional analyses The large genotypic and phenotypic data set generated in this study is a resource that allows a more in-depth exploration of the role of human genetic variation: we ran additional analyses to test whether there is any evidence from the existing data, which mainly represents common variants, for other determinants of viral control. We first performed a genome-wide screen ( Text S1 ) for SNPs that modify the effect of rs2395029 (HCP5/B*5701), rs9264942 (HLA-C) rs9261174 (ZNRD1/RNF39) and CCR5-Δ32: no interaction was large enough to reach genome-wide significance. To evaluate common structural variation, we tested 285 SNPs that were identified as tags for copy number polymorphisms (CNP) in HapMap CEU samples [27]. No significant association was observed when theses CNP-tagging SNPs were considered as a set. We also used a gene set enrichment analysis (GSEA) [28],[29] to ask whether groups of genes or pathways were enriched in SNPs with low association p-values: 5 gene sets were significant, some of them with suggestive evidence of involvement in HIV-1 pathogenesis (Table S7). Finally, we developed a permutation procedure to test whether genetic variants with known functional role were more likely to associate with differences in HIV-1 control than non-functional SNPs: we observed a significant effect that was however limited to the MHC region (p = 0.001) (Figure S4). Discussion The results presented here reaffirm the central role of the MHC in HIV-1 control by first confirming with certainty the independence of two association signals in the genomic region that encompasses HLA-B and HLA-C. These common variants show the strongest association with both viral set point and progression. We initially speculated that the HCP5 gene itself, which contains the top-associated SNP rs2395029, contributes to HIV-1 control [3], but recent epidemiologic and functional reports [30],[31],[32] suggest that the gene and the variant itself have no such effect. In addition, we here present data from a small number of subjects with a recombination event between HCP5 and HLA-B indicating that HLA-B*5701 is the main contributor to the association signal: the HCP5 variant is therefore likely to be only a marker for the effect of HLA-B*5701 and possibly of other protective variants present on the same haplotype. The HLA-C variant rs9264942, which is in partial LD with all HLA-C alleles (Table S6), associates with mRNA and protein expression levels of HLA-C [3],[33] (R. Thomas, M.C., personal communication): it is thus likely that this SNP is in fact a marker of the effect of HLA-C expression on HIV-1 control: more work is needed to understand the precise immunological and biological function of HLA-C in the context of HIV-1 infection. Beyond the top 2 associated variants, we demonstrate that there are other, independent, genetic contributors to HIV-1 control in the MHC. The intricacy of the LD pattern in the region makes it difficult to find causal variants with certainty. Nevertheless, using both SNP and HLA Class I data, we identify additional variants that associate independently with viral set point and together explain at least 3.5% of the variability, on top of the 9% explained by the first 2 SNPs. We do not know at this stage whether any of the 4 SNPs identified in our permutation analysis have a direct functional role, though a non-synonymous coding change in a TRIM gene represents an attractive candidate [34]. Several of the HLA alleles that independently associate with control have good functional support for their involvement in HIV-1 pathogenesis: B*2705 presents epitopes that lead to efficient viral restriction [35], while B*3502 is a member of the B35Px group [36] that, according to recent data obtained on B*3503, has a preferential binding to the inhibitory myelomonocytic MHC class I receptor ILT4 on myeloid dendritic cells, which results in dysfunctional antigen-presenting properties of these cells (XG Yu, personal communication). Functional studies of the gene variants identified in the MHC and deeper understanding of the structure of the associations between SNPs and surrounding HLA alleles [16] are warranted. We show data that question most previously reported associations in HIV-1 candidate genes (Table 3 and [19]). The chemokine/chemokine receptor locus on chromosome 3 is the only non-MHC region with convincing association evidence for an impact of genetic variation on HIV-1 phenotypes. Homozygosity for CCR5Δ32 is known to confer almost complete protection against infection [20],[21],[22] and we here show that heterozygosity for the 32 bp deletion is the strongest protective factor for VL control and progression outside of MHC. While it is possible that our analyses missed some real candidate gene associations, our failure to replicate most previous reports confirms the critical importance of adopting stringent standards for significance level and stratification control [14],[37],[38]. Our study was powered to detect a single marker association that explains just above 1% of the inter-individual variability in HIV-1 control. In the absence of further significant association at the individual SNP level, we sought to comprehensively assess the impact of common variation by using the genotyping results in several additional ways: these analyses provide no evidence for strong interactions between the MHC or CCR5 polymorphisms and any other common variant, or for CNP-related effects. We also used a more global approach that shows enrichment for associated variants in some interesting gene sets, but was not designed to identify novel genetic determinants. Limitations to these additional analyses include [1] the deliberately limited scope of our interaction screen, in which only pairs of SNPs including one that is significant have been tested ( Text S1 ): we did not run any exhaustive SNP by SNP or haplotype analyses; [2] the use of SNP tags for copy number assessment, which limits the analysis to previously described deletions and duplications that are in LD with common SNPs. All variants securely identified in this study together explain 13% of the observed variability in HIV-1 viremia in a population of mostly male Caucasian adults. The addition of gender, age and residual population structure to the genetic model pushes this figure up to 22%. These fractions compare favorably to what is known for other complex traits, where dozens of SNPs often explain only a few percent of the variance. Comparable studies are certainly needed in additional populations, notably in other ethnic groups, in women and in children to fully assess the impact of common human genetic variation in HIV-1 control. Many factors certainly contribute to the large unexplained portion of the inter-individual variability (viral genetics/fitness, environment, stochastic biological variation, noise in phenotype determination), but it is also expected that much more is attributable to human genetic variation. The data presented here suggest that common polymorphisms are unlikely to add much, unless more complex gene by gene and gene-environment interactions play a major role in the genetic architecture of HIV-1 control. After an era of candidate genes studies and a first wave of large-scale genomics projects that could only interrogate common genetic variation, resequencing strategies to identify rare causal variants, as well as integration of multiple genome-level data (genomic DNA, epigenetic marks, transcriptome, siRNA screens) will prove essential to better appreciate the global contribution of the human genome to HIV-1 control. Methods Ethics statement All participating centers provided local institutional review board approval for genetic analysis, and each participant provided informed consent for genetic testing. Patients/cohorts Patients have been included from two sources ( Text S1 ): [1] Euro-CHAVI, a Consortium of 8 European and 1 Australian Cohorts/Studies; and [2] MACS, the Multicenter AIDS Cohort Study that enrolled homosexual and bisexual men in 4 US cities. In general, patients had viral load (VL) and CD4 count monitoring at least 6 monthly. Eligible patients had 3 or more stable plasma HIV RNA results in the absence of antiretroviral treatment, and met one of the following criteria: a valid seroconversion date estimation proven by documents or biological markers; or, for seroprevalent patients, VL data over a period of at least 3 years, diverging by no more than 0.5 log. Only individuals with known date of seroconversion and at least 2 CD4 T cell determinations in absence of potent antiretroviral treatment (cART) were included in the progression analysis. Determination of phenotypes HIV-1 set point was defined as the average of the remaining VL results after careful assessment of each individual data and elimination of VL outliers: see Text S1 for criteria used to identify and exclude outlier VL data. Of note, our phenotype definition leads to the exclusion rapid progressors that never reach a stable VL plateau. The disease progression phenotype was defined as [1] the drop of CD4 T cells below 350/ul, or [2] the initiation of cART, but only if the last CD4 T cell count before cART start was = 1%), as well as subjects that was identified as “female” in the phenotype file but had a high frequency of homozygous X genotypes (> = 80%) and Y genotype readings were excluded. Control for population stratification To control for the possibility of spurious associations resulting from population stratification we used a modified EIGENSTRAT approach. This method derives the principal components of the correlations among gene variants and corrects for those correlations in the association tests. In principle therefore the principal components in the analyses should reflect population ancestry. Having noticed however that some of the leading axes depend on other sources of correlation, such as sets of variants near one another that show extended association (LD), we inspected the SNP loadings and followed a series of pruning procedures to ensure that EIGENSTRAT axes reflected only effects that applied equally across the whole genome ( Text S1 ). Genome-wide association analysis The core association analyses on HIV-1 setpoint focused on single-marker genotype-trend tests of the quality control–passed SNPs using linear regression and including age, gender, and the significant EIGENSTRAT axes values as covariates. Associations with progression were tested using a Cox proportional hazards model. We assessed significance with a Bonferroni correction taking into account 1 million tests (p-value cutoff = 5E–08). Search for independent associations in the MHC To search for additional SNPs effects in the MHC region we used a forward selection algorithm to investigate all MHC SNPs with p 0.05, Hardy-Weinberg Equilibrium test p-value was >0.001, and at least 90% individuals were successfully genotyped. Among the 639 canonical pathway gene sets that are represented in the Molecular Signatures Database (MsigDB, http://www.broad.mit.edu/gsea/msigdb/collections.jsp), we tested the 298 gene sets that had at least 20 and at most 200 genes represented in the study ( Text S1 ). Functional variants A permutation procedure was developed to test whether genetic variants with known functional role were more likely to have a lower p-values distribution than supposedly neutral variants ( Text S1 ). We compared the overall p-value distribution of 12,535 putatively functional polymorphisms, including stop-gained, stop-lost, frame-shift coding, non-synonymous coding, and essential splicing site genetic variants, with the global distribution of p-values of all genotyped SNPs (with 10,000 permutations). We also ran the same analysis for the MHC region only (318 functional variants) and for the rest of the genome (12217 functional variants). Supporting Information Figure S1 Distribution of individual eigen values along the first axis identified by principal component analysis of the genotyping data (Eigenstrat). Participants are grouped by country of recruitment. The most important contributor to population stratification in our Caucasian population is a North-to-South European ancestry axis. Participants from the USA and Australia are most similar to northern Europeans. (0.05 MB DOC) Click here for additional data file. Figure S2 QQ-plot of observed versus expected -log(p-values) for all SNPs in the set point association analysis. The plot shows no deviation from the expected line, except for the top 3000 SNPs (0.3%). (0.03 MB DOC) Click here for additional data file. Figure S3 Associations between the SNPs identified in the ZNRD1/RNF39 region and disease progression in individuals with and without HLA-A alleles belonging to the serogroup A10. The rs9261174 minor allele C shows the strongest association with progression in individuals that also have an HLA-A10 (green), but it also associates when HLA-A10 is absent (red). The LD (r2) between the ZNRD1 SNP rs9261174 and HLA-A10 as a group is 0.46. It is 0.17 for HLA-A*2501, 0.25 for A*2601, 0.00 for A*3402 and 0.02 for A*6601. (0.06 MB DOC) Click here for additional data file. Figure S4 P-value distribution of functional variants. P-value distributions of 12,535 functional genetic variants (red, A) in comparison with 12,535 randomly selected intergenic variants (red, B), both in the background of p-value distributions generated from 10,000 permutations (blue, A and B). The upper figures show the distributions of ranked -log10(P) for each of the observed and permutated p-value series. The lower figures show the distributions of the sum -log10(P) generated from the permutated series (blue), their 95% cutoff of empirical probability (cyan), and the relative position and probability of the observed sum -log10(P) (red) in these distributions. The sum -log10(P) from the observed functional genetic variants (red, A) is higher than what would be expected if there were no enrichment of low p-values in this series, and this phenomenon is very unlikely to be due to chance (p = 0.004), while the data from randomly selected 12,535 intergenic variants shows no difference (p = 0.76). The randomly selected 12,535 intergenic variant series in (B) can be approximately viewed as one of the permutated data series in (A) except that they are restricted to be annotated as intergenic, and is presented here for better illustration of the results. Functional genetic variants are those annotated as falling into one or more of these categories: stop-gained, stop-lost, fame-shift coding, non-synonymous coding, and essential splicing site genetic variants. Ensembl database version 50_36i was used for these annotations. (0.13 MB DOC) Click here for additional data file. Table S1 Participants included in the study. (0.04 MB DOC) Click here for additional data file. Table S2 VL setpoint values for groups of individuals with or without a recombination event between HCP5 and HLA-B. (0.03 MB DOC) Click here for additional data file. Table S3 Results of the stepwise forward selection of MHC SNPs. (0.04 MB DOC) Click here for additional data file. Table S4 Associations between 4-digit HLA Class I alleles and HIV-1 set point in the subset of 1204 subjects with complete SNP and HLA typing results. (0.10 MB DOC) Click here for additional data file. Table S5 Pairs of HLA-B and HLA-C alleles that are in linkage disequilibrium (with an r2 of at least 0.1) in the subset of 1204 individuals with complete HLA typing results. (0.07 MB DOC) Click here for additional data file. Table S6 Associations between HLA-C alleles and HIV-1 set point in the subset of 1204 subjects with complete SNP and HLA typing results. (0.06 MB DOC) Click here for additional data file. Table S7 Results of the GSEA analysis. (0.04 MB DOC) Click here for additional data file. Table S8 Comparison of the strength of the association results for polymorphisms with clear association with HIV outcomes using different definitions of progression in survival analyses. (0.03 MB DOC) Click here for additional data file. Table S9 Top 500 SNPs in the setpoint analysis. (0.74 MB DOC) Click here for additional data file. Table S10 Top 500 SNPs in the progression analysis. (0.72 MB DOC) Click here for additional data file. Table S11 Number of SNPs discarded during quality control procedures. (0.03 MB DOC) Click here for additional data file. Text S1 Supplementary text. (0.09 MB DOC) Click here for additional data file.

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Validation of electronic medical record-based phenotyping algorithms: results and lessons learned from the eMERGE network.

Melissa Basford, Peggy L. Peissig, Jodi L. Berg … (2013)

Genetic studies require precise phenotype definitions, but electronic medical record (EMR) phenotype data are recorded inconsistently and in a variety of formats. To present lessons learned about validation of EMR-based phenotypes from the Electronic Medical Records and Genomics (eMERGE) studies. The eMERGE network created and validated 13 EMR-derived phenotype algorithms. Network sites are Group Health, Marshfield Clinic, Mayo Clinic, Northwestern University, and Vanderbilt University. By validating EMR-derived phenotypes we learned that: (1) multisite validation improves phenotype algorithm accuracy; (2) targets for validation should be carefully considered and defined; (3) specifying time frames for review of variables eases validation time and improves accuracy; (4) using repeated measures requires defining the relevant time period and specifying the most meaningful value to be studied; (5) patient movement in and out of the health plan (transience) can result in incomplete or fragmented data; (6) the review scope should be defined carefully; (7) particular care is required in combining EMR and research data; (8) medication data can be assessed using claims, medications dispensed, or medications prescribed; (9) algorithm development and validation work best as an iterative process; and (10) validation by content experts or structured chart review can provide accurate results. Despite the diverse structure of the five EMRs of the eMERGE sites, we developed, validated, and successfully deployed 13 electronic phenotype algorithms. Validation is a worthwhile process that not only measures phenotype performance but also strengthens phenotype algorithm definitions and enhances their inter-institutional sharing.

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Extracting medication information from clinical text.

Ozlem Uzuner, Imre Solti, Eithon Cadag (2010)

The Third i2b2 Workshop on Natural Language Processing Challenges for Clinical Records focused on the identification of medications, their dosages, modes (routes) of administration, frequencies, durations, and reasons for administration in discharge summaries. This challenge is referred to as the medication challenge. For the medication challenge, i2b2 released detailed annotation guidelines along with a set of annotated discharge summaries. Twenty teams representing 23 organizations and nine countries participated in the medication challenge. The teams produced rule-based, machine learning, and hybrid systems targeted to the task. Although rule-based systems dominated the top 10, the best performing system was a hybrid. Of all medication-related fields, durations and reasons were the most difficult for all systems to detect. While medications themselves were identified with better than 0.75 F-measure by all of the top 10 systems, the best F-measure for durations and reasons were 0.525 and 0.459, respectively. State-of-the-art natural language processing systems go a long way toward extracting medication names, dosages, modes, and frequencies. However, they are limited in recognizing duration and reason fields and would benefit from future research.

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Author and article information

Journal

Journal ID (nlm-ta): Genes Immun

Journal ID (iso-abbrev): Genes Immun

Title: Genes and Immunity

Publisher: Nature Publishing Group

ISSN (Print): 1466-4879

ISSN (Electronic): 1476-5470

Publication date (Print): January 2015

Publication date (Electronic): 09 October 2014

Volume: 16

Issue: 1

Pages: 1-7

Affiliations

[1 ]Division of Medical Genetics, Department of Medicine, University of Washington , Seattle, WA, USA

[2 ]Department of Genome Sciences, University of Washington , Seattle, WA, USA

[3 ]Group Health Research Institute, Group Health Cooperative , Seattle, WA, USA

[4 ]Department of Biostatistics, University of Washington , Seattle, WA, USA

[5 ]Division of Biomedical Statistics and Informatics, Mayo Clinic , Rochester, MN, USA

[6 ]Division of Cardiovascular Diseases, Mayo Clinic , Rochester, MN, USA

[7 ]The Sigfried and Janet Weis Center for Research, Geisinger Health System , Danville, PA, USA

[8 ]Essentia Institute of Rural Health , Duluth, MN, USA

[9 ]Center for Human Genetics, Marshfield Clinic Research Foundation , Marshfield, WI, USA

[10 ]Center for Inherited Disease Research, Johns Hopkins University School of Medicine , Baltimore, MD, USA

[11 ]Divisions of General Internal Medicine and Preventive Medicine, Feinberg School of Medicine, Northwestern University , Chicago, IL, USA

[12 ]Division of Endocrinology, Metabolism, and Molecular Medicine, Feinberg School of Medicine, Northwestern University , Chicago, IL, USA

[13 ]Center for Systems Genomics, Department of Biochemistry and Molecular Biology, Pennsylvania State University , Pennsylvania, PA, USA

[14 ]Department of Biomedical Informatics, Vanderbilt University , Nashville, TN, USA

[15 ]Center for Human Genetics Research, Vanderbilt University , Nashville, TN, USA

[16 ]Department of Molecular Physiology and Biophysics, Vanderbilt University , Nashville, TN, USA

[17 ]Division of General Internal Medicine, University of Washington , Seattle, WA, USA

[18 ]The Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine , New York, NY, USA

[19 ]Division of Genomic Medicine, National Human Genome Research Institute, National Institutes of Health , Bethesda, MD, USA

[20 ]Center for Applied Genomics, Children's Hospital of Philadelphia , Philadelphia, PA, USA

[21 ]Program in Medical and Population Genetics, Broad Institute of Harvard and MIT , Cambridge, MA, USA

[22 ]Fred Hutchinson Cancer Research Center, Public Health Sciences Division , Seattle, WA, USA

[23 ]Cincinnati Children's Hospital Medical Center/Boston's Children's Hospital (CCHMC/BCH) , Boston, MA, USA

Author notes

[* ]Department of Medicine, Division of Medical Genetics, University of Washington , 1705 NE Pacific Street, K253, Box 357720, Seattle, WA 98195, USA. E-mail: davidcr@ 123456u.washington.edu

Article

Publisher Item ID: gene201451

DOI: 10.1038/gene.2014.51

PMC ID: 4308645

PubMed ID: 25297839

SO-VID: 7b73331d-59b7-48c3-85d7-56215b37a547

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This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License. The images or other third party material in this article are included in the article's Creative Commons license, unless indicated otherwise in the credit line; if the material is not included under the Creative Commons license, users will need to obtain permission from the license holder to reproduce the material. To view a copy of this license, visit http://creativecommons.org/licenses/by-nc-sa/4.0/

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Date received : 14 May 2014

Date revision received : 22 July 2014

Date accepted : 24 July 2014

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Validation of electronic medical record-based phenotyping algorithms: results and lessons learned from the eMERGE network.

Extracting medication information from clinical text.

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