Genome-wide pathway analysis of genome-wide association studies on systemic lupus erythematosus and rheumatoid arthritis

The aim of this study was to explore candidate single nucleotide polymorphisms (SNPs) and candidate mechanisms of systemic lupus erythematosus (SLE) and rheumatoid arthritis (RA). Two SLE genome-wide association studies (GWASs) datasets were included in this study. Meta-analysis was conducted using 737,984 SNPs in 1,527 SLE cases and 3,421 controls of European ancestry, and 4,429 SNPs that met a threshold of p?<?0.01 in a Korean RA GWAS dataset was used. ICSNPathway (identify candidate causal SNPs and pathways) analysis was applied to the meta-analysis results of the SLE GWAS datasets, and a RA GWAS dataset. The most significant result of SLE GWAS meta-analysis concerned rs2051549 in the human leukocyte antigen (HLA) region (p?=?3.36E?22). In the non-HLA region, meta-analysis identified 6 SNPs associated with SLE with genome-wide significance (STAT4, TNPO3, BLK, FAM167A, and IRF5). ICSNPathway identified five candidate causal SNPs and 13 candidate causal pathways. This pathway-based analysis provides three hypotheses of the biological mechanism involved. First, rs8084 and rs7192?→?HLA-DRA?→?bystander B cell activation. Second, rs1800629?→?TNF?→?cytokine network. Third, rs1150752 and rs185819?→?TNXB?→?collagen metabolic process. ICSNPathway analysis identified three candidate causal non-HLA SNPs and four candidate causal pathways involving the PADI4, MTR, PADI2, and TPH2 genes of RA. We identified five candidate SNPs and thirteen pathways, involving bystander B cell activation, cytokine network, and collagen metabolic processing, which may contribute to SLE susceptibility, and we revealed candidate causal non-HLA SNPs, genes, and pathways of RA.     

Unifying candidate gene and GWAS Approaches in Asthma

The first genome wide association study (GWAS) for childhood asthma identified a novel major susceptibility locus on chromosome 17q21 harboring the ORMDL3 gene, but the role of previous asthma candidate genes was not specifically analyzed in this GWAS. We systematically identified 89 SNPs in 14 candidate genes previously associated with asthma in >3 independent study populations. We re-genotyped 39 SNPs in these genes not covered by GWAS performed in 703 asthmatics and 658 reference children. Genotyping data were compared to imputation data derived from Illumina HumanHap300 chip genotyping. Results were combined to analyze 566 SNPs covering all 14 candidate gene loci. Genotyped polymorphisms in ADAM33, GSTP1 and VDR showed effects with p-values <0.0035 (corrected for multiple testing). Combining genotyping and imputation, polymorphisms in DPP10, EDN1, IL12B, IL13, IL4, IL4R and TNF showed associations at a significance level between p = 0.05 and p = 0.0035. These data indicate that (a) GWAS coverage is insufficient for many asthma candidate genes, (b) imputation based on these data is reliable but incomplete, and (c) SNPs in three previously identified asthma candidate genes replicate in our GWAS population with significance after correction for multiple testing in 14 genes.     

Association Study of 167 Candidate Genes for Schizophrenia Selected by a Multi-Domain Evidence-Based Prioritization Algorithm and Neurodevelopmental Hypothesis

Integrating evidence from multiple domains is useful in prioritizing disease candidate genes for subsequent testing. We ranked all known human genes (n = 3819) under linkage peaks in the Irish Study of High-Density Schizophrenia Families using three different evidence domains: 1) a meta-analysis of microarray gene expression results using the Stanley Brain collection, 2) a schizophrenia protein-protein interaction network, and 3) a systematic literature search. Each gene was assigned a domain-specific p-value and ranked after evaluating the evidence within each domain. For comparison to this ranking process, a large-scale candidate gene hypothesis was also tested by including genes with Gene Ontology terms related to neurodevelopment. Subsequently, genotypes of 3725 SNPs in 167 genes from a custom Illumina iSelect array were used to evaluate the top ranked vs. hypothesis selected genes. Seventy-three genes were both highly ranked and involved in neurodevelopment (category 1) while 42 and 52 genes were exclusive to neurodevelopment (category 2) or highly ranked (category 3), respectively. The most significant associations were observed in genes PRKG1, PRKCE, and CNTN4 but no individual SNPs were significant after correction for multiple testing. Comparison of the approaches showed an excess of significant tests using the hypothesis-driven neurodevelopment category. Random selection of similar sized genes from two independent genome-wide association studies (GWAS) of schizophrenia showed the excess was unlikely by chance. In a further meta-analysis of three GWAS datasets, four candidate SNPs reached nominal significance. Although gene ranking using integrated sources of prior information did not enrich for significant results in the current experiment, gene selection using an a priori hypothesis (neurodevelopment) was superior to random selection. As such, further development of gene ranking strategies using more carefully selected sources of information is warranted.     

Genome-wide association study of schizophrenia in Japanese population

Schizophrenia is a devastating neuropsychiatric disorder with genetically complex traits. Genetic variants should explain a considerable portion of the risk for schizophrenia, and genome-wide association study (GWAS) is a potentially powerful tool for identifying the risk variants that underlie the disease. Here, we report the results of a three-stage analysis of three independent cohorts consisting of a total of 2,535 samples from Japanese and Chinese populations for searching schizophrenia susceptibility genes using a GWAS approach. Firstly, we examined 115,770 single nucleotide polymorphisms (SNPs) in 120 patient-parents trio samples from Japanese schizophrenia pedigrees. In stage II, we evaluated 1,632 SNPs (1,159 SNPs of p<0.01 and 473 SNPs of p<0.05 that located in previously reported linkage regions). The second sample consisted of 1,012 case-control samples of Japanese origin. The most significant p value was obtained for the SNP in the ELAVL2 [(embryonic lethal, abnormal vision, Drosophila)-like 2] gene located on 9p21.3 (p = 0.00087). In stage III, we scrutinized the ELAVL2 gene by genotyping gene-centric tagSNPs in the third sample set of 293 family samples (1,163 individuals) of Chinese descent and the SNP in the gene showed a nominal association with schizophrenia in Chinese population (p = 0.026). The current data in Asian population would be helpful for deciphering ethnic diversity of schizophrenia etiology.     

Pathway analysis of a genome-wide association study in schizophrenia

Objective

The aim of this study was to identify the candidate single nucleotide polymorphisms (SNPs) and candidate mechanisms that contribute to schizophrenia susceptibility and to generate a SNP to gene to pathway hypothesis using an analytical pathway-based approach.

Methods

We used schizophrenia GWAS data of the genotypes of 660,259 SNPs in 1378 controls and 1351 cases of European descent after quality control filtering. ICSNPathway (Identify candidate Causal SNPs and Pathways) analysis was applied to the schizophrenia GWAS dataset. The first stage involved the pre-selection of candidate SNPs by linkage disequilibrium analysis and the functional SNP annotation of the most significant SNPs found. The second stage involved the annotation of biological mechanisms for the pre-selected candidate SNPs using improved-gene set enrichment analysis.

Results

ICSNPathway analysis identified fifteen candidate SNPs, ten candidate pathways, and nine hypothetical biological mechanisms. The most strongly associated potential pathways were as follows. First, rs1644731 and rs1644730 to RDH8 to estrogen biosynthetic process (p < 0.001, FDR < 0.001). The genes involved in this pathway are RDH8 and HSD3B1 (p < 0.05). All-trans-retinol dehydrogenase (RDH8) is a visual cycle enzyme that reduces all-trans-retinal to all-trans-retinol in the presence of NADPH. The chemical reactions and pathways involved result in the formation of estrogens, which are C18 steroid hormones that can stimulate the development of female sexual characteristics. Second, rs1146031 to ACVR1 to mesoderm formation and activin binding (p < 0.001, FDR = 0.032, 0.034). Two of 15 candidate genes are known genes associated with schizophrenia: KCNQ2 and APOL2. One of the 10 candidate pathways, estrogen biosynthetic process, is known to be associated with schizophrenia (p < 0.001, FDR < 0.001). However, 13 of candidate genes (RDH8, ACVR1, PSMD9, KCNAB1, SLC17A3, ARCN1, COG7, STAB2, LRPAP1, STAB1, CXCL16, COL4A4, EXOSC3) and 9 of candidate pathways were novel.

Conclusion

By applying ICSNPathway analysis to schizophrenia GWAS data, we identified candidate SNPs, genes like KCNQ2 and APOL2 and pathways involving the estrogen biosynthetic process may contribute to schizophrenia susceptibility. Further analyses are needed to validate the results of this analysis.     

Pathway Analysis Based on a Genome-Wide Association Study of Polycystic Ovary Syndrome

Background

Polycystic ovary syndrome (PCOS) is one of the most common endocrine disorders in women of reproductive age, and it is affected by both environmental and genetic factors. Although the genetic component of PCOS is evident, studies aiming to identify susceptibility genes have shown controversial results. This study conducted a pathway-based analysis using a dataset obtained through a genome-wide association study (GWAS) to elucidate the biological pathways that contribute to PCOS susceptibility and the associated genes.

Methods

We used GWAS data on 636,797 autosomal single nucleotide polymorphisms (SNPs) from 1,221 individuals (432 PCOS patients and 789 controls) for analysis. A pathway analysis was conducted using meta-analysis gene-set enrichment of variant associations (MAGENTA). Top-ranking pathways or gene sets associated with PCOS were identified, and significant genes within the pathways were analyzed.

Results

The pathway analysis of the GWAS dataset identified significant pathways related to oocyte meiosis and the regulation of insulin secretion by acetylcholine and free fatty acids (all nominal gene-set enrichment analysis (GSEA) P-values < 0.05). In addition, INS, GNAQ, STXBP1, PLCB3, PLCB2, SMC3 and PLCZ1 were significant genes observed within the biological pathways (all gene P-values < 0.05).

Conclusions

By applying MAGENTA pathway analysis to PCOS GWAS data, we identified significant pathways and candidate genes involved in PCOS. Our findings may provide new leads for understanding the mechanisms underlying the development of PCOS.     

A meta-analysis of two genome-wide association studies to identify novel loci for maximum number of alcoholic drinks

Maximum number of alcoholic drinks consumed in a 24-h period (maxdrinks) is a heritable (>50 %) trait and is strongly correlated with vulnerability to excessive alcohol consumption and subsequent alcohol dependence (AD). Several genome-wide association studies (GWAS) have studied alcohol dependence, but few have concentrated on excessive alcohol consumption. We performed two GWAS using maxdrinks as an excessive alcohol consumption phenotype: one in 118 extended families (N = 2,322) selected from the Collaborative Study on the Genetics of Alcoholism (COGA), and the other in a case–control sample (N = 2,593) derived from the Study of Addiction: Genes and Environment (SAGE). The strongest association in the COGA families was detected with rs9523562 (p = 2.1 × 10^?6) located in an intergenic region on chromosome 13q31.1; the strongest association in the SAGE dataset was with rs67666182 (p = 7.1 × 10^?7), located in an intergenic region on chromosome 8. We also performed a meta-analysis with these two GWAS and demonstrated evidence of association in both datasets for the LMO1 (p = 7.2 × 10^?7) and PLCL1 genes (p = 4.1 × 10^?6) with maxdrinks. A variant in AUTS2 and variants in INADL, C15orf32 and HIP1 that were associated with measures of alcohol consumption in a meta-analysis of GWAS studies and a GWAS of alcohol consumption factor score also showed nominal association in the current meta-analysis. The present study has identified several loci that warrant further examination in independent samples. Among the top SNPs in each of the dataset (p ≤ 10^?4) far more showed the same direction of effect in the other dataset than would be expected by chance (p = 2 × 10^?3, 3 × 10^?6), suggesting that there are true signals among these top SNPs, even though no SNP reached genome-wide levels of significance.     

Beyond Parkinson disease: amyotrophic lateral sclerosis and the axon guidance pathway

Background

We recently described a genomic pathway approach to study complex diseases. We demonstrated that models constructed using single nucleotide polymorphisms (SNPs) within axon guidance pathway genes were highly predictive of Parkinson disease (PD) susceptibility, survival free of PD, and age at onset of PD within two independent whole-genome association datasets. We also demonstrated that several axon guidance pathway genes represented by SNPs within our final models were differentially expressed in PD.

Methodology/Principal Findings

Here we employed our genomic pathway approach to analyze data from a whole-genome association dataset of amyotrophic lateral sclerosis (ALS); and demonstrated that models constructed using SNPs within axon guidance pathway genes were highly predictive of ALS susceptibility (odds ratio = 1739.73, p = 2.92×10⁻⁶⁰), survival free of ALS (hazards ratio = 149.80, p = 1.25×10⁻⁷⁴), and age at onset of ALS (R² = 0.86, p = 5.96×10⁻⁶⁶). We also extended our analyses of a whole-genome association dataset of PD, which shared 320,202 genomic SNPs in common with the whole-genome association dataset of ALS. We compared for ALS and PD the genes represented by SNPs in the final models for susceptibility, survival free of disease, and age at onset of disease and noted that 52.2%, 37.8%, and 34.9% of the genes were shared respectively.

Conclusions/Significance

Our findings for the axon guidance pathway and ALS have prior biological plausibility, overlap partially with PD, and may provide important insight into the causes of these and related neurodegenerative disorders.     

A meta-analysis of genome-wide association scans identifies IL18RAP, PTPN2, TAGAP, and PUS10 as shared risk loci for Crohn's disease and celiac disease

Crohn''s disease (CD) and celiac disease (CelD) are chronic intestinal inflammatory diseases, involving genetic and environmental factors in their pathogenesis. The two diseases can co-occur within families, and studies suggest that CelD patients have a higher risk to develop CD than the general population. These observations suggest that CD and CelD may share common genetic risk loci. Two such shared loci, IL18RAP and PTPN2, have already been identified independently in these two diseases. The aim of our study was to explicitly identify shared risk loci for these diseases by combining results from genome-wide association study (GWAS) datasets of CD and CelD. Specifically, GWAS results from CelD (768 cases, 1,422 controls) and CD (3,230 cases, 4,829 controls) were combined in a meta-analysis. Nine independent regions had nominal association p-value <1.0×10⁻⁵ in this meta-analysis and showed evidence of association to the individual diseases in the original scans (p-value <1×10⁻² in CelD and <1×10⁻³ in CD). These include the two previously reported shared loci, IL18RAP and PTPN2, with p-values of 3.37×10⁻⁸ and 6.39×10⁻⁹, respectively, in the meta-analysis. The other seven had not been reported as shared loci and thus were tested in additional CelD (3,149 cases and 4,714 controls) and CD (1,835 cases and 1,669 controls) cohorts. Two of these loci, TAGAP and PUS10, showed significant evidence of replication (Bonferroni corrected p-values <0.0071) in the combined CelD and CD replication cohorts and were firmly established as shared risk loci of genome-wide significance, with overall combined p-values of 1.55×10⁻¹⁰ and 1.38×10⁻¹¹ respectively. Through a meta-analysis of GWAS data from CD and CelD, we have identified four shared risk loci: PTPN2, IL18RAP, TAGAP, and PUS10. The combined analysis of the two datasets provided the power, lacking in the individual GWAS for single diseases, to detect shared loci with a relatively small effect.     

Network-Based Meta-Analyses of Associations of Multiple Gene Expression Profiles with Bone Mineral Density Variations in Women

Background

Existing microarray studies of bone mineral density (BMD) have been critical for understanding the pathophysiology of osteoporosis, and have identified a number of candidate genes. However, these studies were limited by their relatively small sample sizes and were usually analyzed individually. Here, we propose a novel network-based meta-analysis approach that combines data across six microarray studies to identify functional modules from human protein-protein interaction (PPI) data, and highlight several differentially expressed genes (DEGs) and a functional module that may play an important role in BMD regulation in women.

Methods

Expression profiling studies were identified by searching PubMed, Gene Expression Omnibus (GEO) and ArrayExpress. Two meta-analysis methods were applied across different gene expression profiling studies. The first, a nonparametric Fisher’s method, combined p-values from individual experiments to identify genes with large effect sizes. The second method combined effect sizes from individual datasets into a meta-effect size to gain a higher precision of effect size estimation across all datasets. Genes with Q test’s p-values < 0.05 or I² values > 50% were assessed by a random effects model and the remainder by a fixed effects model. Using Fisher’s combined p-values, functional modules were identified through an integrated analysis of microarray data in the context of large protein–protein interaction (PPI) networks. Two previously published meta-analysis studies of genome-wide association (GWA) datasets were used to determine whether these module genes were genetically associated with BMD. Pathway enrichment analysis was performed with a hypergeometric test.

Results

Six gene expression datasets were identified, which included a total of 249 (129 high BMD and 120 low BMD) female subjects. Using a network-based meta-analysis, a consensus module containing 58 genes (nodes) and 83 edges was detected. Pathway enrichment analysis of the 58 module genes revealed that these genes were enriched in several important KEGG pathways including Osteoclast differentiation, B cell receptor signaling pathway, MAPK signaling pathway, Chemokine signaling pathway and Insulin signaling pathway. The importance of module genes was replicated by demonstrating that most module genes were genetically associated with BMD in the GWAS data sets. Meta-analyses were performed at the individual gene level by combining p-values and effect sizes. Five candidate genes (ESR1, MAP3K3, PYGM, RAC1 and SYK) were identified based on gene expression meta-analysis, and their associations with BMD were also replicated by two BMD meta-analysis studies.

Conclusions

In summary, our network-based meta-analysis not only identified important differentially expressed genes but also discovered biologically meaningful functional modules for BMD determination. Our study may provide novel therapeutic targets for osteoporosis in women.     

Genome-wide interaction-based association analysis identified multiple new susceptibility Loci for common diseases

Genome-wide interaction-based association (GWIBA) analysis has the potential to identify novel susceptibility loci. These interaction effects could be missed with the prevailing approaches in genome-wide association studies (GWAS). However, no convincing loci have been discovered exclusively from GWIBA methods, and the intensive computation involved is a major barrier for application. Here, we developed a fast, multi-thread/parallel program named “pair-wise interaction-based association mapping” (PIAM) for exhaustive two-locus searches. With this program, we performed a complete GWIBA analysis on seven diseases with stringent control for false positives, and we validated the results for three of these diseases. We identified one pair-wise interaction between a previously identified locus, C1orf106, and one new locus, TEC, that was specific for Crohn''s disease, with a Bonferroni corrected P<0.05 (P = 0.039). This interaction was replicated with a pair of proxy linked loci (P = 0.013) on an independent dataset. Five other interactions had corrected P<0.5. We identified the allelic effect of a locus close to SLC7A13 for coronary artery disease. This was replicated with a linked locus on an independent dataset (P = 1.09×10⁻⁷). Through a local validation analysis that evaluated association signals, rather than locus-based associations, we found that several other regions showed association/interaction signals with nominal P<0.05. In conclusion, this study demonstrated that the GWIBA approach was successful for identifying novel loci, and the results provide new insights into the genetic architecture of common diseases. In addition, our PIAM program was capable of handling very large GWAS datasets that are likely to be produced in the future.     

Association of the type 2 diabetes mellitus susceptibility gene, TCF7L2, with schizophrenia in an Arab-Israeli family sample

Many reports in different populations have demonstrated linkage of the 10q24–q26 region to schizophrenia, thus encouraging further analysis of this locus for detection of specific schizophrenia genes. Our group previously reported linkage of the 10q24–q26 region to schizophrenia in a unique, homogeneous sample of Arab-Israeli families with multiple schizophrenia-affected individuals, under a dominant model of inheritance. To further explore this candidate region and identify specific susceptibility variants within it, we performed re-analysis of the 10q24-26 genotype data, taken from our previous genome-wide association study (GWAS) (Alkelai et al, 2011). We analyzed 2089 SNPs in an extended sample of 57 Arab Israeli families (189 genotyped individuals), under the dominant model of inheritance, which best fits this locus according to previously performed MOD score analysis. We found significant association with schizophrenia of the TCF7L2 gene intronic SNP, rs12573128, (p = 7.01×10⁻⁶) and of the nearby intergenic SNP, rs1033772, (p = 6.59×10⁻⁶) which is positioned between TCF7L2 and HABP2. TCF7L2 is one of the best confirmed susceptibility genes for type 2 diabetes (T2D) among different ethnic groups, has a role in pancreatic beta cell function and may contribute to the comorbidity of schizophrenia and T2D. These preliminary results independently support previous findings regarding a possible role of TCF7L2 in susceptibility to schizophrenia, and strengthen the importance of integrating linkage analysis models of inheritance while performing association analyses in regions of interest. Further validation studies in additional populations are required.     

A multi-SNP association test for complex diseases incorporating an optimal P-value threshold algorithm in nuclear families

Background

Genome-wide association studies (GWAS) have become a common approach to identifying single nucleotide polymorphisms (SNPs) associated with complex diseases. As complex diseases are caused by the joint effects of multiple genes, while the effect of individual gene or SNP is modest, a method considering the joint effects of multiple SNPs can be more powerful than testing individual SNPs. The multi-SNP analysis aims to test association based on a SNP set, usually defined based on biological knowledge such as gene or pathway, which may contain only a portion of SNPs with effects on the disease. Therefore, a challenge for the multi-SNP analysis is how to effectively select a subset of SNPs with promising association signals from the SNP set.

Results

We developed the Optimal P-value Threshold Pedigree Disequilibrium Test (OPTPDT). The OPTPDT uses general nuclear families. A variable p-value threshold algorithm is used to determine an optimal p-value threshold for selecting a subset of SNPs. A permutation procedure is used to assess the significance of the test. We used simulations to verify that the OPTPDT has correct type I error rates. Our power studies showed that the OPTPDT can be more powerful than the set-based test in PLINK, the multi-SNP FBAT test, and the p-value based test GATES. We applied the OPTPDT to a family-based autism GWAS dataset for gene-based association analysis and identified MACROD2-AS1 with genome-wide significance (p-value= 2.5 × 10^− 6).

Conclusions

Our simulation results suggested that the OPTPDT is a valid and powerful test. The OPTPDT will be helpful for gene-based or pathway association analysis. The method is ideal for the secondary analysis of existing GWAS datasets, which may identify a set of SNPs with joint effects on the disease.

Electronic supplementary material

The online version of this article (doi:10.1186/s12864-015-1620-3) contains supplementary material, which is available to authorized users.     

Evidence for Association of Cell Adhesion Molecules Pathway and NLGN1 Polymorphisms with Schizophrenia in Chinese Han Population

Multiple risk variants of schizophrenia have been identified by Genome-wide association studies (GWAS). As a complement for GWAS, previous pathway-based analysis has indicated that cell adhesion molecules (CAMs) pathway might be involved in the pathogenesis of schizophrenia. However, less replication studies have been reported. Our objective was to investigate the association between CAMs pathway and schizophrenia in the Chinese Han population. We first performed a pathway analysis utilizing our previous GWAS data. The CAMs pathway (hsa04514) was significantly associated with schizophrenia using hybrid gene set-based test (P = 1.03×10⁻¹⁰) and hypergeometric test (P = 5.04×10⁻⁶). Moreover, 12 genes (HLA-A, HLA-C, HLA-DOB, HLA-DPB1, HLA-DQA2, HLA-DRB1, MPZ, CD276, NLGN1, NRCAM, CLDN1 and ICAM3) were modestly significantly associated with schizophrenia (P<0.01). Then, we selected one promising gene neuroligin 1 (NLGN1) to further investigate the association between eight significant SNPs and schizophrenia in an independent sample (1814 schizophrenia cases and 1487 healthy controls). Our study showed that seven SNPs of NLGN1 and two haplotype blocks were significantly associated with schizophrenia. This association was confirmed by the results of combined analysis. Among them, SNP rs9835385 had the most significant association with schizophrenia (P = 2.83×10⁻⁷). Furthermore, in silico analysis we demonstrated that NLGN1 is preferentially expressed in human brain and SNP rs1488547 was related to the expression level. We validated the association of CAMs pathway with schizophrenia in pathway-level and identified one susceptibility gene NLGN1. Further investigation of the roles of CAMs pathway in the pathogenesis of schizophrenia is warranted.     

A powerful method to integrate genotype and gene expression data for dissecting the genetic architecture of a disease

To decipher the genetic architecture of human disease, various types of omics data are generated. Two common omics data are genotypes and gene expression. Often genotype data for a large number of individuals and gene expression data for a few individuals are generated due to biological and technical reasons, leading to unequal sample sizes for different omics data. Unavailability of standard statistical procedure for integrating such datasets motivates us to propose a two-step multi-locus association method using latent variables. Our method is powerful than single/separate omics data analysis and it unravels comprehensively deep-seated signals through a single statistical model. Extensive simulation confirms that it is robust to various genetic models as its power increases with sample size and number of associated loci. It provides p-values very fast. Application to real dataset on psoriasis identifies 17 novel SNPs, functionally related to psoriasis-associated genes, at much smaller sample size than standard GWAS.     

A genome-wide meta-analysis of six type 1 diabetes cohorts identifies multiple associated loci

Diabetes impacts approximately 200 million people worldwide, of whom approximately 10% are affected by type 1 diabetes (T1D). The application of genome-wide association studies (GWAS) has robustly revealed dozens of genetic contributors to the pathogenesis of T1D, with the most recent meta-analysis identifying in excess of 40 loci. To identify additional genetic loci for T1D susceptibility, we examined associations in the largest meta-analysis to date between the disease and ∼2.54 million SNPs in a combined cohort of 9,934 cases and 16,956 controls. Targeted follow-up of 53 SNPs in 1,120 affected trios uncovered three new loci associated with T1D that reached genome-wide significance. The most significantly associated SNP (rs539514, P = 5.66×10⁻¹¹) resides in an intronic region of the LMO7 (LIM domain only 7) gene on 13q22. The second most significantly associated SNP (rs478222, P = 3.50×10⁻⁹) resides in an intronic region of the EFR3B (protein EFR3 homolog B) gene on 2p23; however, the region of linkage disequilibrium is approximately 800 kb and harbors additional multiple genes, including NCOA1, C2orf79, CENPO, ADCY3, DNAJC27, POMC, and DNMT3A. The third most significantly associated SNP (rs924043, P = 8.06×10⁻⁹) lies in an intergenic region on 6q27, where the region of association is approximately 900 kb and harbors multiple genes including WDR27, C6orf120, PHF10, TCTE3, C6orf208, LOC154449, DLL1, FAM120B, PSMB1, TBP, and PCD2. These latest associated regions add to the growing repertoire of gene networks predisposing to T1D.     

Replication of genetic associations with plasma lipoprotein traits in a multiethnic sample

Recent genome-wide association studies (GWAS) have reproducibly identified loci associated with plasma triglycerides (TG), HDL cholesterol, and LDL cholesterol. We sought to replicate these findings in a multiethnic population-based cohort using the curated single nucleotide polymorphism (SNP) set found on the new Illumina cardiovascular disease (CVD) beadchip, which contains approximately 50,000 SNPs densely mapping approximately 2,100 genes, selected based on their potential role in CVD. The sample consisted of individuals with European (n = 272), South Asian (n = 330), and Chinese (n = 304) ancestry. Identity by state clustering successfully classified individuals according to self-reported ethnicities. Associations between TG and APOA5, TG and LPL, HDL and CETP, and LDL and APOE were all identified (P < 2 × 10⁻⁶). In 13 loci, associations with the same SNP or a proxy SNP were identified in the same direction as previously reported (P < 0.05). Assessing the cumulative number of risk-associated alleles at multiple replicated SNPs increased the proportion of explained lipoprotein variance over and above traditional variables such as age, sex, body mass index, and ethnicity. The findings indicate the potential utility of the Illumina CVD beadchip, but they underscore the need to consider meta-analysis of results from commonly studied clinical or epidemiological samples.