Rare, low-frequency, and common variants in the protein-coding sequence of biological candidate genes from GWASs contribute to risk of rheumatoid arthritis

The extent to which variants in the protein-coding sequence of genes contribute to risk of rheumatoid arthritis (RA) is unknown. In this study, we addressed this issue by deep exon sequencing and large-scale genotyping of 25 biological candidate genes located within RA risk loci discovered by genome-wide association studies (GWASs). First, we assessed the contribution of rare coding variants in the 25 genes to the risk of RA in a pooled sequencing study of 500 RA cases and 650 controls of European ancestry. We observed an accumulation of rare nonsynonymous variants exclusive to RA cases in IL2RA and IL2RB (burden test: p = 0.007 and p = 0.018, respectively). Next, we assessed the aggregate contribution of low-frequency and common coding variants to the risk of RA by dense genotyping of the 25 gene loci in 10,609 RA cases and 35,605 controls. We observed a strong enrichment of coding variants with a nominal signal of association with RA (p < 0.05) after adjusting for the best signal of association at the loci (p(enrichment) = 6.4 × 10(-4)). For one locus containing CD2, we found that a missense variant, rs699738 (c.798C>A [p.His266Gln]), and a noncoding variant, rs624988, reside on distinct haplotypes and independently contribute to the risk of RA (p = 4.6 × 10(-6)). Overall, our results indicate that variants (distributed across the allele-frequency spectrum) within the protein-coding portion of a subset of biological candidate genes identified by GWASs contribute to the risk of RA. Further, we have demonstrated that very large sample sizes will be required for comprehensively identifying the independent alleles contributing to the missing heritability of RA.

Host-microbe interactions have shaped the genetic architecture of inflammatory bowel disease

Crohn’s disease (CD) and ulcerative colitis (UC), the two common forms of inflammatory bowel disease (IBD), affect over 2.5 million people of European ancestry with rising prevalence in other populations¹. Genome-wide association studies (GWAS) and subsequent meta-analyses of CD and UC^2,3 as separate phenotypes implicated previously unsuspected mechanisms, such as autophagy⁴, in pathogenesis and showed that some IBD loci are shared with other inflammatory diseases⁵. Here we expand knowledge of relevant pathways by undertaking a meta-analysis of CD and UC genome-wide association scans, with validation of significant findings in more than 75,000 cases and controls. We identify 71 new associations, for a total of 163 IBD loci that meet genome-wide significance thresholds. Most loci contribute to both phenotypes, and both directional and balancing selection effects are evident. Many IBD loci are also implicated in other immune-mediated disorders, most notably with ankylosing spondylitis and psoriasis. We also observe striking overlap between susceptibility loci for IBD and mycobacterial infection. Gene co-expression network analysis emphasizes this relationship, with pathways shared between host responses to mycobacteria and those predisposing to IBD.

High-density genetic mapping identifies new susceptibility loci for rheumatoid arthritis

Using the Immunochip custom single nucleotide polymorphism (SNP) array, designed for dense genotyping of 186 genome wide association study (GWAS) confirmed loci we analysed 11,475 rheumatoid arthritis cases of European ancestry and 15,870 controls for 129,464 markers. The data were combined in meta-analysis with GWAS data from additional independent cases (n=2,363) and controls (n=17,872). We identified fourteen novel loci; nine were associated with rheumatoid arthritis overall and 5 specifically in anti-citrillunated peptide antibody positive disease, bringing the number of confirmed European ancestry rheumatoid arthritis loci to 46. We refined the peak of association to a single gene for 19 loci, identified secondary independent effects at six loci and association to low frequency variants (minor allele frequency <0.05) at 4 loci. Bioinformatic analysis of the data generated strong hypotheses for the causal SNP at seven loci. This study illustrates the advantages of dense SNP mapping analysis to inform subsequent functional investigations.

Risk for myasthenia gravis maps to a (151) Pro→Ala change in TNIP1 and to human leukocyte antigen-B*08

OBJECTIVE

The objective of this study is to comprehensively define the genetic basis of early onset myasthenia gravis (EOMG).

METHODS

We have carried out a 2-stage genome-wide association study on a total of 649 North European EOMG patients. Cases were matched 1:4 with controls of European ancestry. We performed imputation and conditional analyses across the major histocompatibility complex, as well as in the top regions of association outside the human leukocyte antigen (HLA) region.

RESULTS

We observed the strongest association in the HLA class I region at rs7750641 (p = 1.2 × 10(-92) ; odds ratio [OR], 6.25). By imputation and conditional analyses, HLA-B*08 proves to be the major associated allele (p = 2.87 × 10(-113) ; OR, 6.41). In addition to the expected association with PTPN22 (rs2476601; OR, 1.71; p = 8.2 × 10(-10) ), an imputed coding variant (rs2233290) at position 151 (Pro→Ala) in the TNFAIP3-interacting protein 1, TNIP1, confers even stronger risk than PTPN22 (OR, 1.91; p = 3.2 × 10(-10) ).

INTERPRETATION

The association at TNIP1 in EOMG implies disease mechanisms involving ubiquitin-dependent dysregulation of NF-κB signaling. The localization of the major HLA signal to the HLA-B*08 allele suggests that CD8(+) T cells may play a key role in disease initiation or pathogenesis.

Interrogating the major histocompatibility complex with high-throughput genomics

The major histocompatibility complex (MHC) region on the short arm of chromosome 6 harbors the largest number of replicated associations across the human genome for a wide range of diseases, but the functional basis for these associations is still poorly understood. One fundamental challenge in fine-mapping associations to functional alleles is the enormous sequence diversity and broad linkage disequilibrium of the MHC, both of which hamper the cost-effective interrogation in large patient samples and the identification of causal variants. In this review, we argue that there is now a valuable opportunity to leverage existing genome-wide association study (GWAS) datasets for in-depth investigation to identify independent effects in the MHC. Application of imputation to GWAS data facilitates comprehensive interrogation of the classical human leukocyte antigen (HLA) loci. These datasets are, in many cases, sufficiently large to give investigators the ability to disentangle effects at different loci. We also explain how querying variation at individual amino acid positions for association can be powerful and expand traditional analyses that focus only on the classical HLA types.

Large-scale association analysis provides insights into the genetic architecture and pathophysiology of type 2 diabetes

To extend understanding of the genetic architecture and molecular basis of type 2 diabetes (T2D), we conducted a meta-analysis of genetic variants on the Metabochip, including 34,840 cases and 114,981 controls, overwhelmingly of European descent. We identified ten previously unreported T2D susceptibility loci, including two showing sex-differentiated association. Genome-wide analyses of these data are consistent with a long tail of additional common variant loci explaining much of the variation in susceptibility to T2D. Exploration of the enlarged set of susceptibility loci implicates several processes, including CREBBP-related transcription, adipocytokine signaling and cell cycle regulation, in diabetes pathogenesis.

Heritability and genome-wide association study to assess genetic differences between advanced age-related macular degeneration subtypes

PURPOSE

To investigate whether the 2 subtypes of advanced age-related macular degeneration (AMD), choroidal neovascularization (CNV), and geographic atrophy (GA) segregate separately in families and to identify which genetic variants are associated with these 2 subtypes.

DESIGN

Sibling correlation study and genome-wide association study (GWAS).

PARTICIPANTS

For the sibling correlation study, 209 sibling pairs with advanced AMD were included. For the GWAS, 2594 participants with advanced AMD subtypes and 4134 controls were included. Replication cohorts included 5383 advanced AMD participants and 15 240 controls.

METHODS

Participants had the AMD grade assigned based on fundus photography, examination, or both. To determine heritability of advanced AMD subtypes, a sibling correlation study was performed. For the GWAS, genome-wide genotyping was conducted and 6 036 699 single nucleotide polymorphisms (SNPs) were imputed. Then, the SNPs were analyzed with a generalized linear model controlling for genotyping platform and genetic ancestry. The most significant associations were evaluated in independent cohorts.

MAIN OUTCOME MEASURES

Concordance of advanced AMD subtypes in sibling pairs and associations between SNPs with GA and CNV advanced AMD subtypes.

RESULTS

The difference between the observed and expected proportion of siblings concordant for the same subtype of advanced AMD was different to a statistically significant degree (P = 4.2 × 10(-5)), meaning that in siblings of probands with CNV or GA, the same advanced subtype is more likely to develop. In the analysis comparing participants with CNV to those with GA, a statistically significant association was observed at the ARMS2/HTRA1 locus (rs10490924; odds ratio [OR], 1.47; P = 4.3 × 10(-9)), which was confirmed in the replication samples (OR, 1.38; P = 7.4 × 10(-14) for combined discovery and replication analysis).

CONCLUSIONS

Whether CNV versus GA develops in a patient with AMD is determined in part by genetic variation. In this large GWAS meta-analysis and replication analysis, the ARMS2/HTRA1 locus confers increased risk for both advanced AMD subtypes, but imparts greater risk for CNV than for GA. This locus explains a small proportion of the excess sibling correlation for advanced AMD subtype. Other loci were detected with suggestive associations that differ for advanced AMD subtypes and deserve follow-up in additional studies.

Large-scale association analyses identify new loci influencing glycemic traits and provide insight into the underlying biological pathways

Through genome-wide association meta-analyses of up to 133,010 individuals of European ancestry without diabetes, including individuals newly genotyped using the Metabochip, we have increased the number of confirmed loci influencing glycemic traits to 53, of which 33 also increase type 2 diabetes risk (q < 0.05). Loci influencing fasting insulin concentration showed association with lipid levels and fat distribution, suggesting impact on insulin resistance. Gene-based analyses identified further biologically plausible loci, suggesting that additional loci beyond those reaching genome-wide significance are likely to represent real associations. This conclusion is supported by an excess of directionally consistent and nominally significant signals between discovery and follow-up studies. Functional analysis of these newly discovered loci will further improve our understanding of glycemic control.

Rheumatoid arthritis. Evidence for a genetic component to disease severity in RA

Rheumatoid arthritis (RA) is partly heritable; genetic and serological markers are known to confer risk of developing pathology. But given clinical heterogeneity in RA, can we predict who will develop severe disease? Substantial heritability of erosive progression rates has now been identified, but better prognostic biomarkers remain wanting.

Classical HLA-DRB1 and DPB1 alleles account for HLA associations with primary biliary cirrhosis

Susceptibility to primary biliary cirrhosis (PBC) is strongly associated with HLA region polymorphisms. To determine if associations can be explained by classical HLA determinants we studied Italian 676 cases and 1440 controls with genotyped with dense single nucleotide polymorphisms (SNPs) for which classical HLA alleles and amino acids were imputed. Although previous genome-wide association studies and our results show stronger SNP associations near DQB1, we demonstrate that the HLA signals can be attributed to classical DRB1 and DPB1 genes. Strong support for the predominant role of DRB1 is provided by our conditional analyses. We also demonstrate an independent association of DPB1. Specific HLA-DRB1 genes (*08, *11 and *14) account for most of the DRB1 association signal. Consistent with previous studies, DRB1*08 (p = 1.59 × 10⁻¹¹) was the strongest predisposing allele where as DRB1*11 (p = 1.42 × 10⁻¹⁰) was protective. Additionally DRB1*14 and the DPB1 association (DPB1*03:01) (p = 9.18 × 10⁻⁷) were predisposing risk alleles. No signal was observed in the HLA class 1 or class 3 regions. These findings better define the association of PBC with HLA and specifically support the role of classical HLA-DRB1 and DPB1 genes and alleles in susceptibility to PBC.

Use of a multiethnic approach to identify rheumatoid- arthritis-susceptibility loci, 1p36 and 17q12

We have previously shown that rheumatoid arthritis (RA) risk alleles overlap between different ethnic groups. Here, we utilize a multiethnic approach to show that we can effectively discover RA risk alleles. Thirteen putatively associated SNPs that had not yet exceeded genome-wide significance (p < 5 × 10(-8)) in our previous RA genome-wide association study (GWAS) were analyzed in independent sample sets consisting of 4,366 cases and 17,765 controls of European, African American, and East Asian ancestry. Additionally, we conducted an overall association test across all 65,833 samples (a GWAS meta-analysis plus the replication samples). Of the 13 SNPs investigated, four were significantly below the study-wide Bonferroni corrected p value threshold (p < 0.0038) in the replication samples. Two SNPs (rs3890745 at the 1p36 locus [p = 2.3 × 10(-12)] and rs2872507 at the 17q12 locus [p = 1.7 × 10(-9)]) surpassed genome-wide significance in all 16,659 RA cases and 49,174 controls combined. We used available GWAS data to fine map these two loci in Europeans and East Asians, and we found that the same allele conferred risk in both ethnic groups. A series of bioinformatic analyses identified TNFRSF14-MMEL1 at the 1p36 locus and IKZF3-ORMDL3-GSDMB at the 17q12 locus as the genes most likely associated with RA. These findings demonstrate empirically that a multiethnic approach is an effective strategy for discovering RA risk loci, and they suggest that combining GWASs across ethnic groups represents an efficient strategy for gaining statistical power.

Five amino acids in three HLA proteins explain most of the association between MHC and seropositive rheumatoid arthritis

The genetic association of the major histocompatibility complex (MHC) to rheumatoid arthritis risk has commonly been attributed to alleles in HLA-DRB1. However, debate persists about the identity of the causal variants in HLA-DRB1 and the presence of independent effects elsewhere in the MHC. Using existing genome-wide SNP data in 5,018 individuals with seropositive rheumatoid arthritis (cases) and 14,974 unaffected controls, we imputed and tested classical alleles and amino acid polymorphisms in HLA-A, HLA-B, HLA-C, HLA-DPA1, HLA-DPB1, HLA-DQA1, HLA-DQB1 and HLA-DRB1, as well as 3,117 SNPs across the MHC. Conditional and haplotype analyses identified that three amino acid positions (11, 71 and 74) in HLA-DRβ1 and single-amino-acid polymorphisms in HLA-B (at position 9) and HLA-DPβ1 (at position 9), which are all located in peptide-binding grooves, almost completely explain the MHC association to rheumatoid arthritis risk. This study shows how imputation of functional variation from large reference panels can help fine map association signals in the MHC.

The major risk alleles of age-related macular degeneration (AMD) in CFH do not play a major role in rheumatoid arthritis (RA)

Because activation of the alternative pathway (AP) of the complement system is an important aspect of both age-related macular degeneration (AMD) and rheumatoid arthritis (RA), we wished to address the question whether genetic risk factors of the AP inhibitor complement factor H (CFH) for AMD would also be risk factors for RA. For this purpose we genotyped single nucleotide polymorphisms (SNPs) in a Dutch set of RA patients and controls. Similarly, a meta-analysis using a Spanish cohort of RA as well as six large genome-wide association studies (GWAS) studies was performed. For these SNPs we analysed more than 6000 patients and 20,000 controls. The CFH variants, I62V, Y402H, IVS1 and IVS10, known to associate strongly with AMD, did not show a significant association with the risk of developing RA despite a strong statistical power to detect such differences. In conclusion, the major risk alleles of AMD in CFH do not have a similar effect on developing RA.

A genome-wide association search for type 2 diabetes genes in African Americans

African Americans are disproportionately affected by type 2 diabetes (T2DM) yet few studies have examined T2DM using genome-wide association approaches in this ethnicity. The aim of this study was to identify genes associated with T2DM in the African American population. We performed a Genome Wide Association Study (GWAS) using the Affymetrix 6.0 array in 965 African-American cases with T2DM and end-stage renal disease (T2DM-ESRD) and 1029 population-based controls. The most significant SNPs (n = 550 independent loci) were genotyped in a replication cohort and 122 SNPs (n = 98 independent loci) were further tested through genotyping three additional validation cohorts followed by meta-analysis in all five cohorts totaling 3,132 cases and 3,317 controls. Twelve SNPs had evidence of association in the GWAS (P<0.0071), were directionally consistent in the Replication cohort and were associated with T2DM in subjects without nephropathy (P<0.05). Meta-analysis in all cases and controls revealed a single SNP reaching genome-wide significance (P<2.5×10(-8)). SNP rs7560163 (P = 7.0×10(-9), OR (95% CI) = 0.75 (0.67-0.84)) is located intergenically between RND3 and RBM43. Four additional loci (rs7542900, rs4659485, rs2722769 and rs7107217) were associated with T2DM (P<0.05) and reached more nominal levels of significance (P<2.5×10(-5)) in the overall analysis and may represent novel loci that contribute to T2DM. We have identified novel T2DM-susceptibility variants in the African-American population. Notably, T2DM risk was associated with the major allele and implies an interesting genetic architecture in this population. These results suggest that multiple loci underlie T2DM susceptibility in the African-American population and that these loci are distinct from those identified in other ethnic populations.

A rare penetrant mutation in CFH confers high risk of age-related macular degeneration

Two common variants within CFH, the Y402H^1–4 and the rs1410996 SNPs^5,6, explain 17% of age-related macular degeneration (AMD) liability. However, proof for the involvement of CFH, as opposed to a neighboring transcript, and the potential mechanism of susceptibility alleles are lacking. Assuming that rare functional variants might provide mechanistic insights, we used genotype data and high throughput sequencing to discover a rare high-risk CFH haplotype containing an R1210C mutation. This allele has been implicated previously in atypical hemolytic uremic syndrome, and abrogates C-terminal ligand binding^7,8. Genotyping R1210C in 2,423 AMD cases and 1,122 controls demonstrated high penetrance (present in 40 cases versus 1 control, p=7.0×10⁻⁶) and six year earlier onset of disease (p=2.3×10⁻⁶). This result suggests that loss of function alleles at CFH likely drive AMD risk. This finding represents one of the first instances where a common complex disease variant has led to discovery of a rare penetrant mutation.

Integrating autoimmune risk loci with gene-expression data identifies specific pathogenic immune cell subsets

Although genome-wide association studies have implicated many individual loci in complex diseases, identifying the exact causal alleles and the cell types within which they act remains greatly challenging. To ultimately understand disease mechanism, researchers must carefully conceive functional studies in relevant pathogenic cell types to demonstrate the cellular impact of disease-associated genetic variants. This challenge is highlighted in autoimmune diseases, such as rheumatoid arthritis, where any of a broad range of immunological cell types might potentially be impacted by genetic variation to cause disease. To this end, we developed a statistical approach to identify potentially pathogenic cell types in autoimmune diseases by using a gene-expression data set of 223 murine-sorted immune cells from the Immunological Genome Consortium. We found enrichment of transitional B cell genes in systemic lupus erythematosus (p = 5.9 × 10(-6)) and epithelial-associated stimulated dendritic cell genes in Crohn disease (p = 1.6 × 10(-5)). Finally, we demonstrated enrichment of CD4+ effector memory T cell genes within rheumatoid arthritis loci (p < 10(-6)). To further validate the role of CD4+ effector memory T cells within rheumatoid arthritis, we identified 436 loci that were not yet known to be associated with the disease but that had a statistically suggestive association in a recent genome-wide association study (GWAS) meta-analysis (p(GWAS) < 0.001). Even among these putative loci, we noted a significant enrichment for genes specifically expressed in CD4+ effector memory T cells (p = 1.25 × 10(-4)). These cell types are primary candidates for future functional studies to reveal the role of risk alleles in autoimmunity. Our approach has application in other phenotypes, outside of autoimmunity, where many loci have been discovered and high-quality cell-type-specific gene expression is available.

Integrating autoimmune risk loci with gene-expression data identifies specific pathogenic immune cell subsets

Although genome-wide association studies have implicated many individual loci in complex diseases, identifying the exact causal alleles and the cell types within which they act remains greatly challenging. To ultimately understand disease mechanism, researchers must carefully conceive functional studies in relevant pathogenic cell types to demonstrate the cellular impact of disease-associated genetic variants. This challenge is highlighted in autoimmune diseases, such as rheumatoid arthritis, where any of a broad range of immunological cell types might potentially be impacted by genetic variation to cause disease. To this end, we developed a statistical approach to identify potentially pathogenic cell types in autoimmune diseases by using a gene-expression data set of 223 murine-sorted immune cells from the Immunological Genome Consortium. We found enrichment of transitional B cell genes in systemic lupus erythematosus (p = 5.9 × 10(-6)) and epithelial-associated stimulated dendritic cell genes in Crohn disease (p = 1.6 × 10(-5)). Finally, we demonstrated enrichment of CD4+ effector memory T cell genes within rheumatoid arthritis loci (p < 10(-6)). To further validate the role of CD4+ effector memory T cells within rheumatoid arthritis, we identified 436 loci that were not yet known to be associated with the disease but that had a statistically suggestive association in a recent genome-wide association study (GWAS) meta-analysis (p(GWAS) < 0.001). Even among these putative loci, we noted a significant enrichment for genes specifically expressed in CD4+ effector memory T cells (p = 1.25 × 10(-4)). These cell types are primary candidates for future functional studies to reveal the role of risk alleles in autoimmunity. Our approach has application in other phenotypes, outside of autoimmunity, where many loci have been discovered and high-quality cell-type-specific gene expression is available.

Common variants near FRK/COL10A1 and VEGFA are associated with advanced age-related macular degeneration

Despite significant progress in the identification of genetic loci for age-related macular degeneration (AMD), not all of the heritability has been explained. To identify variants which contribute to the remaining genetic susceptibility, we performed the largest meta-analysis of genome-wide association studies to date for advanced AMD. We imputed 6 036 699 single-nucleotide polymorphisms with the 1000 Genomes Project reference genotypes on 2594 cases and 4134 controls with follow-up replication of top signals in 5640 cases and 52 174 controls. We identified two new common susceptibility alleles, rs1999930 on 6q21-q22.3 near FRK/COL10A1 [odds ratio (OR) 0.87; P = 1.1 × 10⁻⁸] and rs4711751 on 6p12 near VEGFA (OR 1.15; P = 8.7 × 10⁻⁹). In addition to the two novel loci, 10 previously reported loci in ARMS2/HTRA1 (rs10490924), CFH (rs1061170, and rs1410996), CFB (rs641153), C3 (rs2230199), C2 (rs9332739), CFI (rs10033900), LIPC (rs10468017), TIMP3 (rs9621532) and CETP (rs3764261) were confirmed with genome-wide significant signals in this large study. Loci in the recently reported genes ABCA1 and COL8A1 were also detected with suggestive evidence of association with advanced AMD. The novel variants identified in this study suggest that angiogenesis (VEGFA) and extracellular collagen matrix (FRK/COL10A1) pathways contribute to the development of advanced AMD.

Three ulcerative colitis susceptibility loci are associated with primary sclerosing cholangitis and indicate a role for IL2, REL, and CARD9

Primary sclerosing cholangitis (PSC) is a chronic cholestatic liver disease characterized by inflammation and fibrosis of the bile ducts. Both environmental and genetic factors contribute to its pathogenesis. To further clarify its genetic background, we investigated susceptibility loci recently identified for ulcerative colitis (UC) in a large cohort of 1,186 PSC patients and 1,748 controls. Single nucleotide polymorphisms (SNPs) tagging 13 UC susceptibility loci were initially genotyped in 854 PSC patients and 1,491 controls from Benelux (331 cases, 735 controls), Germany (265 cases, 368 controls), and Scandinavia (258 cases, 388 controls). Subsequently, a joint analysis was performed with an independent second Scandinavian cohort (332 cases, 257 controls). SNPs at chromosomes 2p16 (P-value 4.12 × 10(-4) ), 4q27 (P-value 4.10 × 10(-5) ), and 9q34 (P-value 8.41 × 10(-4) ) were associated with PSC in the joint analysis after correcting for multiple testing. In PSC patients without inflammatory bowel disease (IBD), SNPs at 4q27 and 9q34 were nominally associated (P < 0.05). We applied additional in silico analyses to identify likely candidate genes at PSC susceptibility loci. To identify nonrandom, evidence-based links we used GRAIL (Gene Relationships Across Implicated Loci) analysis showing interconnectivity between genes in six out of in total nine PSC-associated regions. Expression quantitative trait analysis from 1,469 Dutch and UK individuals demonstrated that five out of nine SNPs had an effect on cis-gene expression. These analyses prioritized IL2, CARD9, and REL as novel candidates.

CONCLUSION

We have identified three UC susceptibility loci to be associated with PSC, harboring the putative candidate genes REL, IL2, and CARD9. These results add to the scarce knowledge on the genetic background of PSC and imply an important role for both innate and adaptive immunological factors.

VIZ-GRAIL: visualizing functional connections across disease loci

MOTIVATION

As disease loci are rapidly discovered, an emerging challenge is to identify common pathways and biological functionality across loci. Such pathways might point to potential disease mechanisms. One strategy is to look for functionally related or interacting genes across genetic loci. Previously, we defined a statistical strategy, Gene Relationships Across Implicated Loci (GRAIL), to identify whether pair-wise gene relationships defined using PubMed text similarity are enriched across loci. Here, we have implemented VIZ-GRAIL, a software tool to display those relationships and to depict the underlying biological patterns.

RESULTS

Our tool can seamlessly interact with the GRAIL web site to obtain the results of analyses and create easy to read visual displays. To most clearly display results, VIZ-GRAIL arranges genes and genetic loci to minimize intersecting pair-wise gene connections. VIZ-GRAIL can be easily applied to other types of functional connections, beyond those from GRAIL. This method should help investigators appreciate the presence of potentially important common functions across loci.

AVAILABILITY

The GRAIL algorithm is implemented online at http://www.broadinstitute.org/mpg/grail/grail.php. VIZ-GRAIL source-code is at http://www.broadinstitute.org/mpg/grail/vizgrail.html.

Meta-analysis of genome-wide association studies in celiac disease and rheumatoid arthritis identifies fourteen non-HLA shared loci

Epidemiology and candidate gene studies indicate a shared genetic basis for celiac disease (CD) and rheumatoid arthritis (RA), but the extent of this sharing has not been systematically explored. Previous studies demonstrate that 6 of the established non-HLA CD and RA risk loci (out of 26 loci for each disease) are shared between both diseases. We hypothesized that there are additional shared risk alleles and that combining genome-wide association study (GWAS) data from each disease would increase power to identify these shared risk alleles. We performed a meta-analysis of two published GWAS on CD (4,533 cases and 10,750 controls) and RA (5,539 cases and 17,231 controls). After genotyping the top associated SNPs in 2,169 CD cases and 2,255 controls, and 2,845 RA cases and 4,944 controls, 8 additional SNPs demonstrated P<5×10⁻⁸ in a combined analysis of all 50,266 samples, including four SNPs that have not been previously confirmed in either disease: rs10892279 near the DDX6 gene (P_combined = 1.2×10⁻¹²), rs864537 near CD247 (P_combined = 2.2×10⁻¹¹), rs2298428 near UBE2L3 (P_combined = 2.5×10⁻¹⁰), and rs11203203 near UBASH3A (P_combined = 1.1×10⁻⁸). We also confirmed that 4 gene loci previously established in either CD or RA are associated with the other autoimmune disease at combined P<5×10⁻⁸ (SH2B3, 8q24, STAT4, and TRAF1-C5). From the 14 shared gene loci, 7 SNPs showed a genome-wide significant effect on expression of one or more transcripts in the linkage disequilibrium (LD) block around the SNP. These associations implicate antigen presentation and T-cell activation as a shared mechanism of disease pathogenesis and underscore the utility of cross-disease meta-analysis for identification of genetic risk factors with pleiotropic effects between two clinically distinct diseases.

Celiac disease (CD) and rheumatoid arthritis (RA) are two autoimmune diseases characterized by distinct clinical features but increased co-occurrence in families and individuals. Genome-wide association studies (GWAS) performed in CD and RA have identified the HLA region and 26 non-HLA genetic risk loci in each disease. Of the 26 CD and 26 RA risk loci, previous studies have shown that six are shared between the two diseases. In this study we aimed to identify additional shared risk alleles and, in doing so, gain more insight into shared disease pathogenesis. We first empirically investigated the distribution of putative risk alleles from GWAS across both diseases (after removing known risk loci for both diseases). We found that CD risk alleles are non-randomly distributed in the RA GWAS (and vice versa), indicating that CD risk alleles have an increased prior probability of being associated with RA (and vice versa). Next, we performed a GWAS meta-analysis to search for shared risk alleles by combing the RA and CD GWAS, performing both directional and opposite allelic effect analyses, followed by replication testing in independent case-control datasets in both diseases. In addition to the already established six non-HLA shared risk loci, we observed statistically robust associations at eight SNPs, thereby increasing the number of shared non-HLA risk loci to fourteen. Finally, we used gene expression studies and pathway analysis tools to identify the plausible candidate genes in the fourteen associated loci. We observed remarkable overrepresentation of T-cell signaling molecules among the shared genes.

Proteins encoded in genomic regions associated with immune-mediated disease physically interact and suggest underlying biology

Genome-wide association studies (GWAS) have defined over 150 genomic regions unequivocally containing variation predisposing to immune-mediated disease. Inferring disease biology from these observations, however, hinges on our ability to discover the molecular processes being perturbed by these risk variants. It has previously been observed that different genes harboring causal mutations for the same Mendelian disease often physically interact. We sought to evaluate the degree to which this is true of genes within strongly associated loci in complex disease. Using sets of loci defined in rheumatoid arthritis (RA) and Crohn's disease (CD) GWAS, we build protein-protein interaction (PPI) networks for genes within associated loci and find abundant physical interactions between protein products of associated genes. We apply multiple permutation approaches to show that these networks are more densely connected than chance expectation. To confirm biological relevance, we show that the components of the networks tend to be expressed in similar tissues relevant to the phenotypes in question, suggesting the network indicates common underlying processes perturbed by risk loci. Furthermore, we show that the RA and CD networks have predictive power by demonstrating that proteins in these networks, not encoded in the confirmed list of disease associated loci, are significantly enriched for association to the phenotypes in question in extended GWAS analysis. Finally, we test our method in 3 non-immune traits to assess its applicability to complex traits in general. We find that genes in loci associated to height and lipid levels assemble into significantly connected networks but did not detect excess connectivity among Type 2 Diabetes (T2D) loci beyond chance. Taken together, our results constitute evidence that, for many of the complex diseases studied here, common genetic associations implicate regions encoding proteins that physically interact in a preferential manner, in line with observations in Mendelian disease.

Electronic medical records for discovery research in rheumatoid arthritis

OBJECTIVE

Electronic medical records (EMRs) are a rich data source for discovery research but are underutilized due to the difficulty of extracting highly accurate clinical data. We assessed whether a classification algorithm incorporating narrative EMR data (typed physician notes) more accurately classifies subjects with rheumatoid arthritis (RA) compared with an algorithm using codified EMR data alone.

METHODS

Subjects with > or =1 International Classification of Diseases, Ninth Revision RA code (714.xx) or who had anti-cyclic citrullinated peptide (anti-CCP) checked in the EMR of 2 large academic centers were included in an "RA Mart" (n = 29,432). For all 29,432 subjects, we extracted narrative (using natural language processing) and codified RA clinical information. In a training set of 96 RA and 404 non-RA cases from the RA Mart classified by medical record review, we used narrative and codified data to develop classification algorithms using logistic regression. These algorithms were applied to the entire RA Mart. We calculated and compared the positive predictive value (PPV) of these algorithms by reviewing the records of an additional 400 subjects classified as having RA by the algorithms.

RESULTS

A complete algorithm (narrative and codified data) classified RA subjects with a significantly higher PPV of 94% than an algorithm with codified data alone (PPV of 88%). Characteristics of the RA cohort identified by the complete algorithm were comparable to existing RA cohorts (80% women, 63% anti-CCP positive, and 59% positive for erosions).

CONCLUSION

We demonstrate the ability to utilize complete EMR data to define an RA cohort with a PPV of 94%, which was superior to an algorithm using codified data alone.

Twelve type 2 diabetes susceptibility loci identified through large-scale association analysis

By combining genome-wide association data from 8,130 individuals with type 2 diabetes (T2D) and 38,987 controls of European descent and following up previously unidentified meta-analysis signals in a further 34,412 cases and 59,925 controls, we identified 12 new T2D association signals with combined P<5x10(-8). These include a second independent signal at the KCNQ1 locus; the first report, to our knowledge, of an X-chromosomal association (near DUSP9); and a further instance of overlap between loci implicated in monogenic and multifactorial forms of diabetes (at HNF1A). The identified loci affect both beta-cell function and insulin action, and, overall, T2D association signals show evidence of enrichment for genes involved in cell cycle regulation. We also show that a high proportion of T2D susceptibility loci harbor independent association signals influencing apparently unrelated complex traits.

Rheumatoid arthritis risk allele PTPRC is also associated with response to anti-tumor necrosis factor alpha therapy

OBJECTIVE

Anti-tumor necrosis factor alpha (anti-TNF) therapy is a mainstay of treatment in rheumatoid arthritis (RA). The aim of the present study was to test established RA genetic risk factors to determine whether the same alleles also influence the response to anti-TNF therapy.

METHODS

A total of 1,283 RA patients receiving etanercept, infliximab, or adalimumab therapy were studied from among an international collaborative consortium of 9 different RA cohorts. The primary end point compared RA patients with a good treatment response according to the European League Against Rheumatism (EULAR) response criteria (n = 505) with RA patients considered to be nonresponders (n = 316). The secondary end point was the change from baseline in the level of disease activity according to the Disease Activity Score in 28 joints (triangle upDAS28). Clinical factors such as age, sex, and concomitant medications were tested as possible correlates of treatment response. Thirty-one single-nucleotide polymorphisms (SNPs) associated with the risk of RA were genotyped and tested for any association with treatment response, using univariate and multivariate logistic regression models.

RESULTS

Of the 31 RA-associated risk alleles, a SNP at the PTPRC (also known as CD45) gene locus (rs10919563) was associated with the primary end point, a EULAR good response versus no response (odds ratio [OR] 0.55, P = 0.0001 in the multivariate model). Similar results were obtained using the secondary end point, the triangle upDAS28 (P = 0.0002). There was suggestive evidence of a stronger association in autoantibody-positive patients with RA (OR 0.55, 95% confidence interval [95% CI] 0.39-0.76) as compared with autoantibody-negative patients (OR 0.90, 95% CI 0.41-1.99).

CONCLUSION

Statistically significant associations were observed between the response to anti-TNF therapy and an RA risk allele at the PTPRC gene locus. Additional studies will be required to replicate this finding in additional patient collections.

Genome-wide association study meta-analysis identifies seven new rheumatoid arthritis risk loci

To identify new genetic risk factors for rheumatoid arthritis, we conducted a genome-wide association study meta-analysis of 5,539 autoantibody-positive individuals with rheumatoid arthritis (cases) and 20,169 controls of European descent, followed by replication in an independent set of 6,768 rheumatoid arthritis cases and 8,806 controls. Of 34 SNPs selected for replication, 7 new rheumatoid arthritis risk alleles were identified at genome-wide significance (P < 5 x 10(-8)) in an analysis of all 41,282 samples. The associated SNPs are near genes of known immune function, including IL6ST, SPRED2, RBPJ, CCR6, IRF5 and PXK. We also refined associations at two established rheumatoid arthritis risk loci (IL2RA and CCL21) and confirmed the association at AFF3. These new associations bring the total number of confirmed rheumatoid arthritis risk loci to 31 among individuals of European ancestry. An additional 11 SNPs replicated at P < 0.05, many of which are validated autoimmune risk alleles, suggesting that most represent genuine rheumatoid arthritis risk alleles.

The landscape of somatic copy-number alteration across human cancers

A powerful way to discover key genes playing causal roles in oncogenesis is to identify genomic regions that undergo frequent alteration in human cancers. Here, we report high-resolution analyses of somatic copy-number alterations (SCNAs) from 3131 cancer specimens, belonging largely to 26 histological types. We identify 158 regions of focal SCNA that are altered at significant frequency across multiple cancer types, of which 122 cannot be explained by the presence of a known cancer target gene located within these regions. Several gene families are enriched among these regions of focal SCNA, including the BCL2 family of apoptosis regulators and the NF-κB pathway. We show that cancer cells harboring amplifications surrounding the MCL1 and BCL2L1 anti-apoptotic genes depend upon expression of these genes for survival. Finally, we demonstrate that a large majority of SCNAs identified in individual cancer types are present in multiple cancer types.

Recent advances in the genetics of rheumatoid arthritis

PURPOSE OF REVIEW

To review the recently discovered genetic risk loci in rheumatoid arthritis (RA), the pathways they implicate, and the genetic architecture of RA.

RECENT FINDINGS

Since 2008 investigators have identified many common genetic variants that confer disease risk through single nucleotide polymorphism genotyping studies; the list of variants will no doubt continue to expand at a rapid rate as genotyping technologies evolve and case-control sample collections continue to grow. In aggregate, these variants implicate pathways leading to NF-kappaB (nuclear factor kappa-light-chain-enhancer of activated B cells) activation, the interluekin-2 signaling pathway, and T-cell activation.

SUMMARY

Although the effect of any individual variant is modest and even in aggregate considerably less than that of the major histocompatability complex, discovery of recent risk variants suggests immunological processes that are involved in disease pathogenesis.

Association of a single-nucleotide polymorphism in CD40 with the rate of joint destruction in rheumatoid arthritis

OBJECTIVE

The severity of joint destruction in rheumatoid arthritis (RA) is highly variable from patient to patient and is influenced by genetic factors. Genome-wide association studies have enormously boosted the field of the genetics of RA susceptibility, but risk loci for RA severity remain poorly defined. A recent meta-analysis of genome-wide association studies identified 6 genetic regions for susceptibility to autoantibody-positive RA: CD40, KIF5A/PIP4K2C, CDK6, CCL21, PRKCQ, and MMEL1/TNFRSF14. The purpose of this study was to investigate whether these newly described genetic regions are associated with the rate of joint destruction.

METHODS

RA patients enrolled in the Leiden Early Arthritis Clinic were studied (n=563). Yearly radiographs were scored using the Sharp/van der Heijde method (median followup 5 years; maximum followup 9 years). The rate of joint destruction between genotype groups was compared using a linear mixed model, correcting for age, sex, and treatment strategies. A total of 393 anti-citrullinated protein antibody (ACPA)-positive RA patients from the North American Rheumatoid Arthritis Consortium (NARAC) who had radiographic data available were used for the replication study.

RESULTS

The TT and CC/CG genotypes of 2 single-nucleotide polymorphisms, rs4810485 (CD40) and rs42041 (CDK6), respectively, were associated with a higher rate of joint destruction in ACPA-positive RA patients (P=0.003 and P=0.012, respectively), with rs4810485 being significant after Bonferroni correction for multiple testing. The association of the CD40 minor allele with the rate of radiographic progression was replicated in the NARAC cohort (P=0.021).

CONCLUSION

A polymorphism in the CD40 locus is associated with the rate of joint destruction in patients with ACPA-positive RA. Our findings provide one of the first non-HLA-related genetic severity factors that has been replicated.

The PRL -1149 G/T polymorphism and rheumatoid arthritis susceptibility

OBJECTIVE

Previous studies have demonstrated that the PRL -1149 T (minor) allele decreases prolactin expression and may be associated with autoimmune disease. The aim of this study was to determine the role of the PRL -1149 G/T polymorphism (rs1341239) in rheumatoid arthritis (RA) susceptibility.

METHODS

We examined the association between PRL -1149 G/T and RA risk in 4 separate study populations, consisting of a total of 3,405 RA cases and 4,111 controls of self-reported white European ancestry. Samples were genotyped using 1 of 3 genotyping platforms, and strict quality control metrics were applied. We tested for association using a 2-tailed Cochran-Mantel-Haenszel additive, fixed-effects model.

RESULTS

In the individual populations, odds ratios (ORs) for an association between PRL -1149 T and RA risk ranged from 0.80 to 0.97. In a joint meta-analysis across all 4 populations, the OR for an association between PRL -1149 T and RA risk was 0.90 (95% confidence interval 0.84-0.96, P=0.001).

CONCLUSION

Our findings indicate a possible association between the PRL -1149 T allele and decreased RA risk. The effect size is small but similar to ORs for other genetic polymorphisms associated with complex traits, including RA.

Meta-analysis of genome scans and replication identify CD6, IRF8 and TNFRSF1A as new multiple sclerosis susceptibility loci

We report the results of a meta-analysis of genome-wide association scans for multiple sclerosis (MS) susceptibility that includes 2,624 subjects with MS and 7,220 control subjects. Replication in an independent set of 2,215 subjects with MS and 2,116 control subjects validates new MS susceptibility loci at TNFRSF1A (combined P = 1.59 x 10(-11)), IRF8 (P = 3.73 x 10(-9)) and CD6 (P = 3.79 x 10(-9)). TNFRSF1A harbors two independent susceptibility alleles: rs1800693 is a common variant with modest effect (odds ratio = 1.2), whereas rs4149584 is a nonsynonymous coding polymorphism of low frequency but with stronger effect (allele frequency = 0.02; odds ratio = 1.6). We also report that the susceptibility allele near IRF8, which encodes a transcription factor known to function in type I interferon signaling, is associated with higher mRNA expression of interferon-response pathway genes in subjects with MS.

Practical aspects of imputation-driven meta-analysis of genome-wide association studies

Motivated by the overwhelming success of genome-wide association studies, droves of researchers are working vigorously to exchange and to combine genetic data to expediently discover genetic risk factors for common human traits. The primary tools that fuel these new efforts are imputation, allowing researchers who have collected data on a diversity of genotype platforms to share data in a uniformly exchangeable format, and meta-analysis for pooling statistical support for a genotype-phenotype association. As many groups are forming collaborations to engage in these efforts, this review collects a series of guidelines, practical detail and learned experiences from a variety of individuals who have contributed to the subject.

Common variants at CD40 and other loci confer risk of rheumatoid arthritis

To identify rheumatoid arthritis risk loci in European populations, we conducted a meta-analysis of two published genome-wide association (GWA) studies totaling 3,393 cases and 12,462 controls. We genotyped 31 top-ranked SNPs not previously associated with rheumatoid arthritis in an independent replication of 3,929 autoantibody-positive rheumatoid arthritis cases and 5,807 matched controls from eight separate collections. We identified a common variant at the CD40 gene locus (rs4810485, P = 0.0032 replication, P = 8.2 x 10(-9) overall, OR = 0.87). Along with other associations near TRAF1 (refs. 2,3) and TNFAIP3 (refs. 4,5), this implies a central role for the CD40 signaling pathway in rheumatoid arthritis pathogenesis. We also identified association at the CCL21 gene locus (rs2812378, P = 0.00097 replication, P = 2.8 x 10(-7) overall), a gene involved in lymphocyte trafficking. Finally, we identified evidence of association at four additional gene loci: MMEL1-TNFRSF14 (rs3890745, P = 0.0035 replication, P = 1.1 x 10(-7) overall), CDK6 (rs42041, P = 0.010 replication, P = 4.0 x 10(-6) overall), PRKCQ (rs4750316, P = 0.0078 replication, P = 4.4 x 10(-6) overall), and KIF5A-PIP4K2C (rs1678542, P = 0.0026 replication, P = 8.8 x 10(-8) overall).

Uptake and trafficking of exogenous sterols in Saccharomyces cerevisiae

The proper distribution of sterols among organelles is critical for numerous cellular functions. How sterols are sorted and moved among membranes remains poorly understood, but they are transported not only in vesicles but also by non-vesicular pathways. One of these pathways moves exogenous sterols from the plasma membrane to the endoplasmic reticulum in the yeast Saccharomyces cerevisiae. We have found that two classes of proteins play critical roles in this transport, ABC transporters (ATP-binding-cassette transporters) and oxysterol-binding protein-related proteins. Transport is also regulated by phosphoinositides and the interactions of sterols with other lipids. Here, we summarize these findings and speculate on the role of non-vesicular sterol transfer in determining intracellular sterol distribution and membrane function.

Development of a CTL vaccine for Her-2/neu using peptide-microspheres and adjuvants

With the ultimate goal of developing a therapeutic cancer vaccine, we encapsulated the Her-2/neu peptide p369-377 in poly(lactide-co-glycolide) microspheres. This formulation was found to effectively elicit CD8+ cytotoxic T cell (CTL) responses in an HLA-A*0201 transgenic mouse model. In contrast, immunization with either peptide alone or peptide formulated in incomplete Freund's adjuvant (IFA) failed to elicit such CTL responses. Responses induced by the peptide-microsphere formulation were found to peak at approximately 6 weeks post-immunization, and were enhanced by delivering increased doses of peptide and with repeated administrations over time. Co-administration of the peptide-microspheres with adjuvants, including granulocyte-macrophage colony stimulating factor, MPL adjuvant and select synthetic Toll-Like Receptor 4 ligands, the aminoalkyl glucosaminide-4 phosphates, significantly augmented CTL responses. These studies provide important guidance for the design of human clinical trials of microsphere vaccines in terms of optimal peptide-microsphere formulation, vaccination regimen, vaccine dose, and adjuvant selection.

Investigating hypoxic tumor physiology through gene expression patterns

Clinical evidence shows that tumor hypoxia is an independent prognostic indicator of poor patient outcome. Hypoxic tumors have altered physiologic processes, including increased regions of angiogenesis, increased local invasion, increased distant metastasis and altered apoptotic programs. Since hypoxia is a potent controller of gene expression, identifying hypoxia-regulated genes is a means to investigate the molecular response to hypoxic stress. Traditional experimental approaches have identified physiologic changes in hypoxic cells. Recent studies have identified hypoxia-responsive genes that may define the mechanism(s) underlying these physiologic changes. For example, the regulation of glycolytic genes by hypoxia can explain some characteristics of the Warburg effect. The converse of this logic is also true. By identifying new classes of hypoxia-regulated gene(s), we can infer the physiologic pressures that require the induction of these genes and their protein products. Furthermore, these physiologically driven hypoxic gene expression changes give us insight as to the poor outcome of patients with hypoxic tumors. Approximately 1-1.5% of the genome is transcriptionally responsive to hypoxia. However, there is significant heterogeneity in the transcriptional response to hypoxia between different cell types. Moreover, the coordinated change in the expression of families of genes supports the model of physiologic pressure leading to expression changes. Understanding the evolutionary pressure to develop a 'hypoxic response' provides a framework to investigate the biology of the hypoxic tumor microenvironment.

The computational analysis of scientific literature to define and recognize gene expression clusters

A limitation of many gene expression analytic approaches is that they do not incorporate comprehensive background knowledge about the genes into the analysis. We present a computational method that leverages the peer-reviewed literature in the automatic analysis of gene expression data sets. Including the literature in the analysis of gene expression data offers an opportunity to incorporate functional information about the genes when defining expression clusters. We have created a method that associates gene expression profiles with known biological functions. Our method has two steps. First, we apply hierarchical clustering to the given gene expression data set. Secondly, we use text from abstracts about genes to (i) resolve hierarchical cluster boundaries to optimize the functional coherence of the clusters and (ii) recognize those clusters that are most functionally coherent. In the case where a gene has not been investigated and therefore lacks primary literature, articles about well-studied homologous genes are added as references. We apply our method to two large gene expression data sets with different properties. The first contains measurements for a subset of well-studied Saccharomyces cerevisiae genes with multiple literature references, and the second contains newly discovered genes in Drosophila melanogaster; many have no literature references at all. In both cases, we are able to rapidly define and identify the biologically relevant gene expression profiles without manual intervention. In both cases, we identified novel clusters that were not noted by the original investigators.

A literature-based method for assessing the functional coherence of a gene group

MOTIVATION

Many experimental and algorithmic approaches in biology generate groups of genes that need to be examined for related functional properties. For example, gene expression profiles are frequently organized into clusters of genes that may share functional properties. We evaluate a method, neighbor divergence per gene (NDPG), that uses scientific literature to assess whether a group of genes are functionally related. The method requires only a corpus of documents and an index connecting the documents to genes.

RESULTS

We evaluate NDPG on 2796 functional groups generated by the Gene Ontology consortium in four organisms: mouse, fly, worm and yeast. NDPG finds functional coherence in 96, 92, 82 and 45% of the groups (at 99.9% specificity) in yeast, mouse, fly and worm respectively.

Identification of osteopontin as a prognostic plasma marker for head and neck squamous cell carcinomas

PURPOSE

Tumor hypoxia modifies treatment efficacy and promotes tumor progression. Here, we investigated the relationship between osteopontin (OPN), tumor pO(2), and prognosis in patients with head and neck squamous cell carcinomas (HNSCC).

EXPERIMENTAL DESIGN

We performed linear discriminant analysis, a machine learning algorithm, on the NCI-60 cancer cell line microarray expression database to identify a gene profile that best distinguish cell lines with high Von-Hippel Lindau (VHL) gene expression, an important regulator of hypoxia-related genes, from those with low expression. Plasma OPN levels in 15 volunteers, 31 VHL patients, and 54 HNSCC patients were quantitatively measured by ELISA. The relationships between plasma OPN levels, tumor pO(2) as measured by the Eppendorf microelectrode, freedom from relapse (FFR), and survival in HNSCC patients were evaluated.

RESULTS

Microarray analysis indicated that OPN gene expression inversely correlated with that of VHL. These findings were confirmed by Northern blot analysis. ELISA studies and Western blot in a HNSCC cell line demonstrated that hypoxia exposure resulted in increased OPN secretion. Patients with VHL syndrome had significantly higher plasma OPN levels than healthy volunteers. Plasma OPN level inversely correlated with tumor pO(2) (P = 0.003, r = -0.42). OPN levels correlated with clinical outcomes. The 1-year FFR and survival rates were 80 and 100%, respectively, for patients with OPN levels <or=450 ng/ml and 43 and 63%, respectively, for levels >450 ng/ml (P = 0.002 and 0.0005). Multivariate analysis revealed that OPN was an independent predictor for FFR and survival.

CONCLUSIONS

Plasma OPN levels appeared to correlate with tumor hypoxia in HNSCC patients and may serve as noninvasive tests to identify patients at high risk for tumor recurrence.

Using text analysis to identify functionally coherent gene groups

The analysis of large-scale genomic information (such as sequence data or expression patterns) frequently involves grouping genes on the basis of common experimental features. Often, as with gene expression clustering, there are too many groups to easily identify the functionally relevant ones. One valuable source of information about gene function is the published literature. We present a method, neighbor divergence, for assessing whether the genes within a group share a common biological function based on their associated scientific literature. The method uses statistical natural language processing techniques to interpret biological text. It requires only a corpus of documents relevant to the genes being studied (e.g., all genes in an organism) and an index connecting the documents to appropriate genes. Given a group of genes, neighbor divergence assigns a numerical score indicating how "functionally coherent" the gene group is from the perspective of the published literature. We evaluate our method by testing its ability to distinguish 19 known functional gene groups from 1900 randomly assembled groups. Neighbor divergence achieves 79% sensitivity at 100% specificity, comparing favorably to other tested methods. We also apply neighbor divergence to previously published gene expression clusters to assess its ability to recognize gene groups that had been manually identified as representative of a common function.

Determining the genomic locations of repetitive DNA sequences with a whole-genome microarray: IS6110 in Mycobacterium tuberculosis

The mycobacterial insertion sequence IS6110 has been exploited extensively as a clonal marker in molecular epidemiologic studies of tuberculosis. In addition, it has been hypothesized that this element is an important driving force behind genotypic variability that may have phenotypic consequences. We present here a novel, DNA microarray-based methodology, designated SiteMapping, that simultaneously maps the locations and orientations of multiple copies of IS6110 within the genome. To investigate the sensitivity, accuracy, and limitations of the technique, it was applied to eight Mycobacterium tuberculosis strains for which complete or partial IS6110 insertion site information had been determined previously. SiteMapping correctly located 64% (38 of 59) of the IS6110 copies predicted by restriction fragment length polymorphism analysis. The technique is highly specific; 97% of the predicted insertion sites were true insertions. Eight previously unknown insertions were identified and confirmed by PCR or sequencing. The performance could be improved by modifications in the experimental protocol and in the approach to data analysis. SiteMapping has general applicability and demonstrates an expansion in the applications of microarrays that complements conventional approaches in the study of genome architecture.