Genevieve L Wojcik

Genome-wide association study of spontaneous resolution of hepatitis C virus infection: data from multiple cohorts.

UNLABELLED Chinese translation BACKGROUND Hepatitis C virus (HCV) infections occur worldwide and either spontaneously resolve or persist and markedly increase the persons lifetime risk for cirrhosis and hepatocellular carcinoma. Although HCV persistence occurs more often in persons of African ancestry and persons with genetic variants near interleukin-28B (IL-28B), the genetic basis is not well-understood. OBJECTIVE To evaluate the host genetic basis for spontaneous resolution of HCV infection. DESIGN 2-stage, genome-wide association study. SETTING 13 international multicenter study sites. PATIENTS 919 persons with serum HCV antibodies but no HCV RNA (spontaneous resolution) and 1482 persons with serum HCV antibodies and HCV RNA (persistence). MEASUREMENTS Frequencies of 792 721 single nucleotide polymorphisms (SNPs). RESULTS Differences in allele frequencies between persons with spontaneous resolution and persistence were identified on chromosomes 19q13.13 and 6p21.32. On chromosome 19, allele frequency differences localized near IL-28B and included rs12979860 (overall per-allele OR, 0.45; P = 2.17 × 10-30) and 10 additional SNPs spanning 55 000 base pairs. On chromosome 6, allele frequency differences localized near genes for HLA class II and included rs4273729 (overall per-allele OR, 0.59; P = 1.71 × 10-16) near DQB1*03:01 and an additional 116 SNPs spanning 1 090 000 base pairs. The associations in chromosomes 19 and 6 were independent and additive and explain an estimated 14.9% (95% CI, 8.5% to 22.6%) and 15.8% (CI, 4.4% to 31.0%) of the variation in HCV resolution in persons of European and African ancestry, respectively. Replication of the chromosome 6 SNP, rs4272729, in an additional 745 persons confirmed the findings (P = 0.015). LIMITATION Epigenetic effects were not studied. CONCLUSION IL-28B and HLA class II are independently associated with spontaneous resolution of HCV infection, and SNPs marking IL-28B and DQB1*03:01 may explain approximately 15% of spontaneous resolution of HCV infection.

Human Demographic History Impacts Genetic Risk Prediction across Diverse Populations

The vast majority of genome-wide association studies (GWASs) are performed in Europeans, and their transferability to other populations is dependent on many factors (e.g., linkage disequilibrium, allele frequencies, genetic architecture). As medical genomics studies become increasingly large and diverse, gaining insights into population history and consequently the transferability of disease risk measurement is critical. Here, we disentangle recent population history in the widely used 1000 Genomes Project reference panel, with an emphasis on populations underrepresented in medical studies. To examine the transferability of single-ancestry GWASs, we used published summary statistics to calculate polygenic risk scores for eight well-studied phenotypes. We identify directional inconsistencies in all scores; for example, height is predicted to decrease with genetic distance from Europeans, despite robust anthropological evidence that West Africans are as tall as Europeans on average. To gain deeper quantitative insights into GWAS transferability, we developed a complex trait coalescent-based simulation framework considering effects of polygenicity, causal allele frequency divergence, and heritability. As expected, correlations between true and inferred risk are typically highest in the population from which summary statistics were derived. We demonstrate that scores inferred from European GWASs are biased by genetic drift in other populations even when choosing the same causal variants and that biases in any direction are possible and unpredictable. This work cautions that summarizing findings from large-scale GWASs may have limited portability to other populations using standard approaches and highlights the need for generalized risk prediction methods and the inclusion of more diverse individuals in medical genomics.

Genome-wide association study of hepatitis C virus- and cryoglobulin-related vasculitis

The host genetic basis of mixed cryoglobulin vasculitis is not well understood and has not been studied in large cohorts. A genome-wide association study was conducted among 356 hepatitis C virus (HCV) RNA-positive individuals with cryoglobulin-related vasculitis and 447 ethnically matched, HCV RNA-positive controls. All cases had both serum cryoglobulins and a vasculitis syndrome. A total of 899 641 markers from the Illumina HumanOmni1-Quad chip were analyzed using logistic regression adjusted for sex, as well as genetically determined ancestry. Replication of select single-nucleotide polymorphisms (SNPs) was conducted using 91 cases and 180 controls, adjusting for sex and country of origin. The most significant associations were identified on chromosome 6 near the NOTCH4 and MHC class II genes. A genome-wide significant association was detected on chromosome 6 at SNP rs9461776 (odds ratio=2.16, P=1.16E−07) between HLA-DRB1 and DQA1: this association was further replicated in additional independent samples (meta-analysis P=7.1 × 10−9). A genome-wide significant association with cryoglobulin-related vasculitis was identified with SNPs near NOTCH4 and MHC Class II genes. The two regions are correlated and it is difficult to disentangle which gene is responsible for the association with mixed cryoglobulinemia vasculitis in this extended major histocompatibility complex region.

Relative performance of gene- and pathway-level methods as secondary analyses for genome-wide association studies

BackgroundDespite the success of genome-wide association studies (GWAS), there still remains “missing heritability” for many traits. One contributing factor may be the result of examining one marker at a time as opposed to a group of markers that are biologically meaningful in aggregate. To address this problem, a variety of gene- and pathway-level methods have been developed to identify putative biologically relevant associations. A simulation was conducted to systematically assess the performance of these methods. Using genetic data from 4,500 individuals in the Wellcome Trust Case Control Consortium (WTCCC), case–control status was simulated based on an additive polygenic model. We evaluated gene-level methods based on their sensitivity, specificity, and proportion of false positives. Pathway-level methods were evaluated on the relationship between proportion of causal genes within the pathway and the strength of association.ResultsThe gene-level methods had low sensitivity (20-63%), high specificity (89-100%), and low proportion of false positives (0.1-6%). The gene-level program VEGAS using only the top 10% of associated single nucleotide polymorphisms (SNPs) within the gene had the highest sensitivity (28.6%) with less than 1% false positives. The performance of the pathway-level methods depended on their reliance upon asymptotic distributions or if significance was estimated in a competitive manner. The pathway-level programs GenGen, GSA-SNP and MAGENTA had the best performance while accounting for potential confounders.ConclusionsNovel genes and pathways can be identified using the gene and pathway-level methods. These methods may provide valuable insight into the “missing heritability” of traits and provide biological interpretations to GWAS findings.

Polymorphisms in Toll-like receptor genes influence antibody responses to cytomegalovirus glycoprotein B vaccine

BackgroundCongenital Cytomegalovirus (CMV) infection is an important medical problem that has yet no current solution. A clinical trial of CMV glycoprotein B (gB) vaccine in young women showed promising efficacy. Improved understanding of the basis for prevention of CMV infection is essential for developing improved vaccines.ResultsWe genotyped 142 women previously vaccinated with three doses of CMV gB for single nucleotide polymorphisms (SNPs) in TLR 1-4, 6, 7, 9, and 10, and their associated intracellular signaling genes. SNPs in the platelet-derived growth factor receptor (PDGFRA) and integrins were also selected based on their role in binding gB. Specific SNPs in TLR7 and IKBKE (inhibitor of nuclear factor kappa-B kinase subunit epsilon) were associated with antibody responses to gB vaccine. Homozygous carriers of the minor allele at four SNPs in TLR7 showed higher vaccination-induced antibody responses to gB compared to heterozygotes or homozygotes for the common allele. SNP rs1953090 in IKBKE was associated with changes in antibody level from second to third dose of vaccine; homozygotes for the minor allele exhibited lower antibody responses while homozygotes for the major allele showed increased responses over time.ConclusionsThese data contribute to our understanding of the immunogenetic mechanisms underlying variations in the immune response to CMV vaccine.

Identification of functional genetic variation in exome sequence analysis.

Recent technological advances have allowed us to study individual genomes at a base-pair resolution and have demonstrated that the average exome harbors more than 15,000 genetic variants. However, our ability to understand the biological significance of the identified variants and to connect these observed variants with phenotypes is limited. The first step in this process is to identify genetic variation that is likely to result in changes to protein structure and function, because detailed studies, either population based or functional, for each of the identified variants are not practicable. Therefore algorithms that yield valid predictions of a variant’s functional significance are needed. Over the past decade, several programs have been developed to predict the probability that an observed sequence variant will have a deleterious effect on protein function. These algorithms range from empirical programs that classify using known biochemical properties to statistical algorithms trained using a variety of data sources, including sequence conservation data, biochemical properties, and functional data. Using data from the pilot3 study of the 1000 Genomes Project available through Genetic Analysis Workshop 17, we compared the results of four programs (SIFT, PolyPhen, MAPP, and VarioWatch) used to predict the functional relevance of variants in 101 genes. Analysis was conducted without knowledge of the simulation model. Agreement between programs was modest ranging from 59.4% to 71.4% and only 3.5% of variants were classified as deleterious and 10.9% as tolerated across all four programs.

Variants in HAVCR1 Gene Region Contribute to Hepatitis C Persistence in African Americans

To confirm previously identified polymorphisms in HAVCR1 that were associated with persistent hepatitis C virus (HCV) infection in individuals of African and of European descent, we studied 165 subjects of African descent and 635 subjects of European descent. Because the association was only confirmed in subjects of African descent (rs6880859; odds ratio, 2.42; P = .01), we then used 379 subjects of African descent (142 with spontaneous HCV clearance) to fine-map HAVCR1. rs111511318 was strongly associated with HCV persistence after adjusting for IL28B and HLA (adjusted P = 8.8 × 10(-4)), as was one 81-kb haplotype (adjusted P = .0006). The HAVCR1 genomic region is an independent genetic determinant of HCV persistence in individuals of African descent.

Role of nucleotide-binding oligomerization domain 1 (NOD1) and its variants in human cytomegalovirus control in vitro and in vivo

Significance Infection with human cytomegalovirus (HCMV) is a growing health problem, creating diagnostic and therapeutic challenges. Biomarkers for risk of infection are lacking, and the limited drugs that inhibit HCMV have major side effects. New strategies for virus control are needed. We report on the role of nucleotide-binding oligomerization domain 1 (NOD1), a cytoplasmic pattern recognition receptor, in HCMV suppression. NOD1 activation (through IKKα and IRF3) resulted in IFN response and HCMV inhibition. Specific mutations in NOD1 showed differential effects on HCMV replication in vitro. In a nested study of HCMV vaccine, specific polymorphisms in NOD1 were detected in HCMV-infected women compared with noninfected women. Our work provides direction for studies of innate immune response to HCMV and genetic susceptibility through NOD1. Induction of nucleotide-binding oligomerization domain 2 (NOD2) and downstream receptor-interacting serine/threonine-protein kinase 2 (RIPK2) by human cytomegalovirus (HCMV) is known to up-regulate antiviral responses and suppress virus replication. We investigated the role of nucleotide-binding oligomerization domain 1 (NOD1), which also signals through RIPK2, in HCMV control. NOD1 activation by Tri-DAP (NOD1 agonist) suppressed HCMV and induced IFN-β. Mouse CMV was also inhibited through NOD1 activation. NOD1 knockdown (KD) or inhibition of its activity with small molecule ML130 enhanced HCMV replication in vitro. NOD1 mutations displayed differential effects on HCMV replication and antiviral responses. In cells overexpressing the E56K mutation in the caspase activation and recruitment domain, virus replication was enhanced, but in cells overexpressing the E266K mutation in the nucleotide-binding domain or the wild-type NOD1, HCMV was inhibited, changes that correlated with IFN-β expression. The interaction of NOD1 and RIPK2 determined the outcome of virus replication, as evidenced by enhanced virus growth in NOD1 E56K mutant cells (which failed to interact with RIPK2). NOD1 activities were executed through IFN-β, given that IFN-β KD reduced the inhibitory effect of Tri-DAP on HCMV. Signaling through NOD1 resulting in HCMV suppression was IKKα-dependent and correlated with nuclear translocation and phosphorylation of IRF3. Finally, NOD1 polymorphisms were significantly associated with the risk of HCMV infection in women who were infected with HCMV during participation in a glycoprotein B vaccine trial. Collectively, our data indicate a role for NOD1 in HCMV control via RIPK2- IKKα-IRF3 and suggest that its polymorphisms predict the risk of infection.

Genetic identification of a common collagen disease in puerto ricans via identity-by-descent mapping in a health system

Achieving confidence in the causality of a disease locus is a complex task that often requires supporting data from both statistical genetics and clinical genomics. Here we describe a combined approach to identify and characterize a genetic disorder that leverages distantly related patients in a health system and population-scale mapping. We utilize genomic data to uncover components of distant pedigrees, in the absence of recorded pedigree information, in the multi-ethnic BioMe biobank in New York City. By linking to medical records, we discover a locus associated with both elevated genetic relatedness and extreme short stature. We link the gene, COL27A1, with a little-known genetic disease, previously thought to be rare and recessive. We demonstrate that disease manifests in both heterozygotes and homozygotes, indicating a common collagen disorder impacting up to 2% of individuals of Puerto Rican ancestry, leading to a better understanding of the continuum of complex and Mendelian disease.

Strategies for Enriching Variant Coverage in Candidate Disease Loci on a Multiethnic Genotyping Array.

Investigating genetic architecture of complex traits in ancestrally diverse populations is imperative to understand the etiology of disease. However, the current paucity of genetic research in people of African and Latin American ancestry, Hispanic and indigenous peoples in the United States is likely to exacerbate existing health disparities for many common diseases. The Population Architecture using Genomics and Epidemiology, Phase II (PAGE II), Study was initiated in 2013 by the National Human Genome Research Institute to expand our understanding of complex trait loci in ethnically diverse and well characterized study populations. To meet this goal, the Multi-Ethnic Genotyping Array (MEGA) was designed to substantially improve fine-mapping and functional discovery by increasing variant coverage across multiple ethnicities at known loci for metabolic, cardiovascular, renal, inflammatory, anthropometric, and a variety of lifestyle traits. Studying the frequency distribution of clinically relevant mutations, putative risk alleles, and known functional variants across multiple populations will provide important insight into the genetic architecture of complex diseases and facilitate the discovery of novel, sometimes population-specific, disease associations. DNA samples from 51,650 self-identified African ancestry (17,328), Hispanic/Latino (22,379), Asian/Pacific Islander (8,640), and American Indian (653) and an additional 2,650 participants of either South Asian or European ancestry, and other reference panels have been genotyped on MEGA by PAGE II. MEGA was designed as a new resource for studying ancestrally diverse populations. Here, we describe the methodology for selecting trait-specific content for use in multi-ethnic populations and how enriching MEGA for this content may contribute to deeper biological understanding of the genetic etiology of complex disease.