Ultrahigh-dimensional variable selection method for whole-genome gene-gene interaction analysis

ABSTRACT: BACKGROUND: Genome-wide gene-gene interaction analysis using single nucleotide polymorphisms (SNPs) is an attractive way for identification of genetic components that confers susceptibility of human complex diseases. Individual hypothesis testing for SNP-SNP pairs as in common genome-wide association study (GWAS) however involves difficulty in setting overall p-value due to complicated correlation structure, namely, the multiple testing problem that causes unacceptable false negative results. A large number of SNP-SNP pairs than sample size, so-called the large p small n problem, precludes simultaneous analysis using multiple regression. The method that overcomes above issues is thus needed. RESULTS: We adopt an up-to-date method for ultrahigh-dimensional variable selection termed the sure independence screening (SIS) [17] for appropriate handling of numerous number of SNP-SNP interactions by including them as predictor variables in logistic regression. We propose ranking strategy using promising dummy coding methods and following variable selection procedure in the SIS method suitably modified for gene-gene interaction analysis. We also implemented the procedures in a software program, EPISIS, using the cost-effective GPGPU (General-purpose computing on graphics processing units) technology. EPISIS can complete exhaustive search for SNP-SNP interactions in standard GWAS dataset within several hours. The proposed method works successfully in simulation experiments and in application to real WTCCC (Wellcome Trust Case-Control Consortium) data. CONCLUSIONS: Based on the machine-learning principle, the proposed method gives powerful and flexible genome-wide search for various patterns of gene-gene interaction.     

Common variants in CDKN2B-AS1 associated with optic-nerve vulnerability of glaucoma identified by genome-wide association studies in Japanese

Background

To date, only a small portion of the genetic variation for primary open-angle glaucoma (POAG), the major type of glaucoma, has been elucidated.

Methods and Principal Findings

We examined our two data sets of the genome-wide association studies (GWAS) derived from a total of 2,219 Japanese subjects. First, we performed a GWAS by analyzing 653,519 autosomal common single-nucleotide polymorphisms (SNPs) in 833 POAG patients and 686 controls. As a result, five variants that passed the Bonferroni correction were identified in CDKN2B-AS1 on chromosome 9p21.3, which was already reported to be a significant locus in the Caucasian population. Moreover, we combined the data set with our previous GWAS data set derived from 411 POAG patients and 289 controls by the Mantel-Haenszel test, and all of the combined variants showed stronger association with POAG (P<5.8×10⁻¹⁰). We then subdivided the case groups into two subtypes based on the value of intraocular pressure (IOP)—POAG with high IOP (high pressure glaucoma, HPG) and that with normal IOP (normal pressure glaucoma, NPG)—and performed the GWAS using the two data sets, as the prevalence of NPG in Japanese is much higher than in Caucasians. The results suggested that the variants from the same CDKN2B-AS1 locus were likely to be significant for NPG patients.

Conclusions and Significance

In this study, we successfully identified POAG-associated variants in the CDKN2B-AS1 locus using a Japanese population, i.e., variants originally reported as being associated with the Caucasian population. Although we cannot rule out that the significance could be due to the differences in sample size between HPG and NPG, the variants could be associated specifically with the vulnerability of the optic nerve to IOP, which is useful for investigating the etiology of glaucoma.     

Novel EDGE encoding method enhances ability to identify genetic interactions

Assumptions are made about the genetic model of single nucleotide polymorphisms (SNPs) when choosing a traditional genetic encoding: additive, dominant, and recessive. Furthermore, SNPs across the genome are unlikely to demonstrate identical genetic models. However, running SNP-SNP interaction analyses with every combination of encodings raises the multiple testing burden. Here, we present a novel and flexible encoding for genetic interactions, the elastic data-driven genetic encoding (EDGE), in which SNPs are assigned a heterozygous value based on the genetic model they demonstrate in a dataset prior to interaction testing. We assessed the power of EDGE to detect genetic interactions using 29 combinations of simulated genetic models and found it outperformed the traditional encoding methods across 10%, 30%, and 50% minor allele frequencies (MAFs). Further, EDGE maintained a low false-positive rate, while additive and dominant encodings demonstrated inflation. We evaluated EDGE and the traditional encodings with genetic data from the Electronic Medical Records and Genomics (eMERGE) Network for five phenotypes: age-related macular degeneration (AMD), age-related cataract, glaucoma, type 2 diabetes (T2D), and resistant hypertension. A multi-encoding genome-wide association study (GWAS) for each phenotype was performed using the traditional encodings, and the top results of the multi-encoding GWAS were considered for SNP-SNP interaction using the traditional encodings and EDGE. EDGE identified a novel SNP-SNP interaction for age-related cataract that no other method identified: rs7787286 (MAF: 0.041; intergenic region of chromosome 7)–rs4695885 (MAF: 0.34; intergenic region of chromosome 4) with a Bonferroni LRT p of 0.018. A SNP-SNP interaction was found in data from the UK Biobank within 25 kb of these SNPs using the recessive encoding: rs60374751 (MAF: 0.030) and rs6843594 (MAF: 0.34) (Bonferroni LRT p: 0.026). We recommend using EDGE to flexibly detect interactions between SNPs exhibiting diverse action.     

A Novel Statistic for Genome-Wide Interaction Analysis

Although great progress in genome-wide association studies (GWAS) has been made, the significant SNP associations identified by GWAS account for only a few percent of the genetic variance, leading many to question where and how we can find the missing heritability. There is increasing interest in genome-wide interaction analysis as a possible source of finding heritability unexplained by current GWAS. However, the existing statistics for testing interaction have low power for genome-wide interaction analysis. To meet challenges raised by genome-wide interactional analysis, we have developed a novel statistic for testing interaction between two loci (either linked or unlinked). The null distribution and the type I error rates of the new statistic for testing interaction are validated using simulations. Extensive power studies show that the developed statistic has much higher power to detect interaction than classical logistic regression. The results identified 44 and 211 pairs of SNPs showing significant evidence of interactions with FDR<0.001 and 0.001<FDR<0.003, respectively, which were seen in two independent studies of psoriasis. These included five interacting pairs of SNPs in genes LST1/NCR3, CXCR5/BCL9L, and GLS2, some of which were located in the target sites of miR-324-3p, miR-433, and miR-382, as well as 15 pairs of interacting SNPs that had nonsynonymous substitutions. Our results demonstrated that genome-wide interaction analysis is a valuable tool for finding remaining missing heritability unexplained by the current GWAS, and the developed novel statistic is able to search significant interaction between SNPs across the genome. Real data analysis showed that the results of genome-wide interaction analysis can be replicated in two independent studies.     

Variants in the ASB10 Gene Are Associated with Primary Open Angle Glaucoma

Background

Recently nonsynonymous coding variants in the ankyrin repeats and suppressor of cytokine signaling box-containing protein 10 (ASB10) gene were found to be associated with primary open angle glaucoma (POAG) in cohorts from Oregon and Germany, but this finding was not confirmed in an independent cohort from Iowa. The aim of the current study was to assess the role of ASB10 gene variants in Pakistani glaucoma patients.

Methods

Sanger sequencing of the coding exons and splice junctions of the ASB10 gene was performed in 30 probands of multiplex POAG families, 208 sporadic POAG patients and 151 healthy controls from Pakistan. Genotypic associations of individual variants with POAG were analyzed with the Fisher’s exact or Chi-square test.

Results

In total 24 variants were identified in POAG probands and sporadic patients, including 11 novel variants and 13 known variants. 13 of the variants were nonsynonymous, 6 were synonymous, and 5 were intronic. Three nonsynonymous variants (p.Arg49Cys, p.Arg237Gly, p.Arg453Cys) identified in the probands were not segregating in the respective families. This is not surprising since glaucoma is a multifactorial disease, and multiple factors are likely to be involved in the disease manifestation in these families. However a nonsynonymous variant, p.Arg453Cys (rs3800791), was found in 6 sporadic POAG patients but not in controls, suggesting that it infers increased risk for the disease. In addition, one synonymous variant was found to be associated with sporadic POAG: p.Ala290Ala and the association of the variant with POAG remained significant after correction for multiple testing (uncorrected p-value 0.002, corrected p-value 0.047). The cumulative burden of rare, nonsynonymous variants was significantly higher in sporadic POAG patients compared to control individuals (p-value 0.000006).

Conclusions

Variants in ASB10 were found to be significantly associated with sporadic POAG in the Pakistani population. This supports previous findings that sequence variants in the ASB10 gene may act as a risk factor for glaucoma.     

Robust Gene-Gene Interaction Analysis in Genome Wide Association Studies

Genome-wide association studies (GWAS) have successfully discovered hundreds of associations between genetic variants and complex traits. Most GWAS have focused on the identification of single variants. It has been shown that most of the variants that were discovered by GWAS could only partially explain disease heritability. The explanation for this missing heritability is generally believed to be gene-gene (GG) or gene-environment (GE) interactions and other structural variants. Generalized multifactor dimensionality reduction (GMDR) has been proven to be reasonably powerful in detecting GG and GE interactions; however, its performance has been found to decline when outlying quantitative traits are present. This paper proposes a robust GMDR estimation method (based on the L-estimator and M-estimator estimation methods) in an attempt to reduce the effects caused by outlying traits. A comparison of robust GMDR with the original MDR based on simulation studies showed the former method to outperform the latter. The performance of robust GMDR is illustrated through a real GWA example consisting of 8,577 samples from the Korean population using the Homeostasis Model Assessment of Insulin Resistance (HOMA-IR) level as a phenotype. Robust GMDR identified the KCNH1 gene to have strong interaction effects with other genes on the function of insulin secretion.     

Genome-wide two-locus epistasis scans in prostate cancer using two European populations

Approximately 40 single nucleotide polymorphisms (SNPs) that are associated with prostate cancer (PCa) risk have been identified through genome-wide association studies (GWAS). However, these GWAS-identified PCa risk-associated SNPs can explain only a small proportion of heritability (~13%) of PCa risk. Gene-gene interaction is speculated to be one of the major factors contributing to the so-called missing heritability. To evaluate the gene-gene interaction and PCa risk, we performed a two-stage genome-wide gene-gene interaction scan using a novel statistical approach named "Boolean Operation-based Screening and Testing". In the first stage, we exhaustively evaluated all pairs of SNP-SNP interactions for ~500,000 SNPs in 1,176 PCa cases and 1,101 control subjects from the National Cancer Institute Cancer Genetic Markers of Susceptibility (CGEMS) study. No SNP-SNP interaction reached a genome-wide significant level of 4.4E-13. The second stage of the study involved evaluation of the top 1,325 pairs of SNP-SNP interactions (P(interaction) <1.0E-08) implicated in CGEMS in another GWAS population of 1,964 PCa cases from the Johns Hopkins Hospital (JHH) and 3,172 control subjects from the Illumina iControl database. Sixteen pairs of SNP-SNP interactions were significant in the JHH population at a P(interaction) cutoff of 0.01. However, none of the 16 pairs of SNP-SNP interactions were significant after adjusting for multiple tests. The current study represents one of the first attempts to explore the high-dimensional etiology of PCa on a genome-wide scale. Our results suggested a list of SNP-SNP interactions that can be followed in other replication studies.     

GxGrare: gene-gene interaction analysis method for rare variants from high-throughput sequencing data

Background

With the rapid advancement of array-based genotyping techniques, genome-wide association studies (GWAS) have successfully identified common genetic variants associated with common complex diseases. However, it has been shown that only a small proportion of the genetic etiology of complex diseases could be explained by the genetic factors identified from GWAS. This missing heritability could possibly be explained by gene-gene interaction (epistasis) and rare variants. There has been an exponential growth of gene-gene interaction analysis for common variants in terms of methodological developments and practical applications. Also, the recent advancement of high-throughput sequencing technologies makes it possible to conduct rare variant analysis. However, little progress has been made in gene-gene interaction analysis for rare variants.

Results

Here, we propose GxGrare which is a new gene-gene interaction method for the rare variants in the framework of the multifactor dimensionality reduction (MDR) analysis. The proposed method consists of three steps; 1) collapsing the rare variants, 2) MDR analysis for the collapsed rare variants, and 3) detect top candidate interaction pairs. GxGrare can be used for the detection of not only gene-gene interactions, but also interactions within a single gene. The proposed method is illustrated with 1080 whole exome sequencing data of the Korean population in order to identify causal gene-gene interaction for rare variants for type 2 diabetes.

Conclusion

The proposed GxGrare performs well for gene-gene interaction detection with collapsing of rare variants. GxGrare is available at http://bibs.snu.ac.kr/software/gxgrare which contains simulation data and documentation. Supported operating systems include Linux and OS X.

     

Finding Missing Heritability in Less Significant Loci and Allelic Heterogeneity: Genetic Variation in Human Height

Genome-wide association studies (GWAS) have identified many common variants associated with complex traits in human populations. Thus far, most reported variants have relatively small effects and explain only a small proportion of phenotypic variance, leading to the issues of ‘missing’ heritability and its explanation. Using height as an example, we examined two possible sources of missing heritability: first, variants with smaller effects whose associations with height failed to reach genome-wide significance and second, allelic heterogeneity due to the effects of multiple variants at a single locus. Using a novel analytical approach we examined allelic heterogeneity of height-associated loci selected from SNPs of different significance levels based on the summary data of the GIANT (stage 1) studies. In a sample of 1,304 individuals collected from an island population of the Adriatic coast of Croatia, we assessed the extent of height variance explained by incorporating the effects of less significant height loci and multiple effective SNPs at the same loci. Our results indicate that approximately half of the 118 loci that achieved stringent genome-wide significance (p-value<5×10⁻⁸) showed evidence of allelic heterogeneity. Additionally, including less significant loci (i.e., p-value<5×10⁻⁴) and accounting for effects of allelic heterogeneity substantially improved the variance explained in height.     

Gene-environment interactions of CETP gene variation in a high cardiovascular risk Mediterranean population

Genome-wide association studies show that cholesteryl ester transfer protein (CETP) single nucleotide polymorphisms (SNPs) are more strongly associated with HDL cholesterol (HDL-C) concentrations than any other loci across the genome. However, gene-environment interactions for clinical applications are still largely unknown. We studied gene-environment interactions between CETP SNPs and dietary fat intake, adherence to the Mediterranean diet, alcohol consumption, smoking, obesity, and diabetes on HDL-C in 4,210 high cardiovascular risk subjects from a Mediterranean population. We focused on the −4,502C>T and the TaqIB SNPs in partial linkage disequilibrium (D''= 0.88; P < 0.001). They were independently associated with higher HDL-C (P < 0.001); this clinically relevant association was greater when their diplotype was considered (14% higher in TT/B2B2 vs. CC/B1B1). No gene-gene interaction was observed. We also analyzed the association of these SNPs with blood pressure, and no clinically relevant associations were detected. No statistically significant interactions of these SNPs with obesity, diabetes, and smoking in determining HDL-C concentrations were found. Likewise, alcohol, dietary fat, and adherence to the Mediterranean diet did not statistically interact with the CETP variants (independently or as diplotype) in determining HDL-C. In conclusion, the strong association of the CETP SNPs and HDL-C was not statistically modified by diet or by the other environmental factors.     

Pathway analysis of genome-wide association study for bone mineral density

The aim of this study was to identify the candidate causal single nucleotide polymorphisms (SNPs) and candidate causal mechanisms that contribute to bone mineral density (BMD) and to generate a SNP to gene to pathway hypothesis using an analytical pathway-based approach. We used hip BMD GWAS data of the genotypes of 301,019 SNPs in 5,715 Europeans. ICSNPathway (identify candidate causal SNPs and pathways) analysis was applied to the BMD GWAS dataset. The first stage involved the pre-selection of candidate causal 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 causal SNPs using improved-gene set enrichment analysis. ICSNPathway analysis identified seven candidate SNPs, eight candidate pathways, and seven hypothetical biological mechanisms. Eight pathways are as follows; gamma-hexachlorocyclohexane degradation (nominal p-value < 0.001, false discovery rate (FDR) <0.001), regulation of the smoothened signaling pathway (nominal p-value < 0.001, FDR = 0.016), TACI and BCMA stimulation of B cell immune response (nominal p-value < 0.001, FDR = 0.021), endonuclease activity (nominal p-value = 0.001, FDR = 0,026), regulation of defense response to virus (nominal p-value = 0.001, FDR = 0.028), serine_type_endopeptidase_inhibitor_activity (nominal p-value = 0.001, FDR = 0.044), endoribonuclease activity (nominal p-value = 0.002, FDR = 0.045), and myeloid leukocyte differentiation (nominal p-value = 0.001, FDR = 0.050). The most significant causal pathway was gamma-hexachlorocyclohexane degradation. CYP3A5, PON2, PON3, CMBL, PON1, ALPL, CYP3A43, CYP3A7, ACP6, ACPP, and ALPI (p < 0.05) are involved in the pathway of gamma-hexachlorocyclohexane degradation. Further examination of the gene contents revealed that DBR1, DICER1, EXO1, FEN1, POP1, POP4, RPP30, and RPP38 were involved in 2 of the 8 pathways (p < 0.05). By applying ICSNPathway analysis to BMD GWAS data, we identified seven candidate SNPs and eight pathways involving gamma-hexachlorocyclohexane degradation, which may contribute to low BMD.     

Connecting the Dots: Potential of Data Integration to Identify Regulatory SNPs in Late-Onset Alzheimer's Disease GWAS Findings

Late-onset Alzheimer''s disease (LOAD) is a multifactorial disorder with over twenty loci associated with disease risk. Given the number of genome-wide significant variants that fall outside of coding regions, it is possible that some of these variants alter some function of gene expression rather than tagging coding variants that alter protein structure and/or function. RegulomeDB is a database that annotates regulatory functions of genetic variants. In this study, we utilized RegulomeDB to investigate potential regulatory functions of lead single nucleotide polymorphisms (SNPs) identified in five genome-wide association studies (GWAS) of risk and age-at onset (AAO) of LOAD, as well as SNPs in LD (r²≥0.80) with the lead GWAS SNPs. Of a total 614 SNPs examined, 394 returned RegulomeDB scores of 1–6. Of those 394 variants, 34 showed strong evidence of regulatory function (RegulomeDB score <3), and only 3 of them were genome-wide significant SNPs (ZCWPW1/rs1476679, CLU/rs1532278 and ABCA7/rs3764650). This study further supports the assumption that some of the non-coding GWAS SNPs are true associations rather than tagged associations and demonstrates the application of RegulomeDB to GWAS data.     

Quantifying Missing Heritability at Known GWAS Loci

Recent work has shown that much of the missing heritability of complex traits can be resolved by estimates of heritability explained by all genotyped SNPs. However, it is currently unknown how much heritability is missing due to poor tagging or additional causal variants at known GWAS loci. Here, we use variance components to quantify the heritability explained by all SNPs at known GWAS loci in nine diseases from WTCCC1 and WTCCC2. After accounting for expectation, we observed all SNPs at known GWAS loci to explain more heritability than GWAS-associated SNPs on average (). For some diseases, this increase was individually significant: for Multiple Sclerosis (MS) () and for Crohn''s Disease (CD) (); all analyses of autoimmune diseases excluded the well-studied MHC region. Additionally, we found that GWAS loci from other related traits also explained significant heritability. The union of all autoimmune disease loci explained more MS heritability than known MS SNPs () and more CD heritability than known CD SNPs (), with an analogous increase for all autoimmune diseases analyzed. We also observed significant increases in an analysis of Rheumatoid Arthritis (RA) samples typed on ImmunoChip, with more heritability from all SNPs at GWAS loci () and more heritability from all autoimmune disease loci () compared to known RA SNPs (including those identified in this cohort). Our methods adjust for LD between SNPs, which can bias standard estimates of heritability from SNPs even if all causal variants are typed. By comparing adjusted estimates, we hypothesize that the genome-wide distribution of causal variants is enriched for low-frequency alleles, but that causal variants at known GWAS loci are skewed towards common alleles. These findings have important ramifications for fine-mapping study design and our understanding of complex disease architecture.     

Knowledge-driven multi-locus analysis reveals gene-gene interactions influencing HDL cholesterol level in two independent EMR-linked biobanks

Genome-wide association studies (GWAS) are routinely being used to examine the genetic contribution to complex human traits, such as high-density lipoprotein cholesterol (HDL-C). Although HDL-C levels are highly heritable (h²∼0.7), the genetic determinants identified through GWAS contribute to a small fraction of the variance in this trait. Reasons for this discrepancy may include rare variants, structural variants, gene-environment (GxE) interactions, and gene-gene (GxG) interactions. Clinical practice-based biobanks now allow investigators to address these challenges by conducting GWAS in the context of comprehensive electronic medical records (EMRs). Here we apply an EMR-based phenotyping approach, within the context of routine care, to replicate several known associations between HDL-C and previously characterized genetic variants: CETP (rs3764261, p = 1.22e-25), LIPC (rs11855284, p = 3.92e-14), LPL (rs12678919, p = 1.99e-7), and the APOA1/C3/A4/A5 locus (rs964184, p = 1.06e-5), all adjusted for age, gender, body mass index (BMI), and smoking status. By using a novel approach which censors data based on relevant co-morbidities and lipid modifying medications to construct a more rigorous HDL-C phenotype, we identified an association between HDL-C and TRIB1, a gene which previously resisted identification in studies with larger sample sizes. Through the application of additional analytical strategies incorporating biological knowledge, we further identified 11 significant GxG interaction models in our discovery cohort, 8 of which show evidence of replication in a second biobank cohort. The strongest predictive model included a pairwise interaction between LPL (which modulates the incorporation of triglyceride into HDL) and ABCA1 (which modulates the incorporation of free cholesterol into HDL). These results demonstrate that gene-gene interactions modulate complex human traits, including HDL cholesterol.     

From Interaction to Co-Association —A Fisher r-To-z Transformation-Based Simple Statistic for Real World Genome-Wide Association Study

Currently, the genetic variants identified by genome wide association study (GWAS) generally only account for a small proportion of the total heritability for complex disease. One crucial reason is the underutilization of gene-gene joint effects commonly encountered in GWAS, which includes their main effects and co-association. However, gene-gene co-association is often customarily put into the framework of gene-gene interaction vaguely. From the causal graph perspective, we elucidate in detail the concept and rationality of gene-gene co-association as well as its relationship with traditional gene-gene interaction, and propose two Fisher r-to-z transformation-based simple statistics to detect it. Three series of simulations further highlight that gene-gene co-association refers to the extent to which the joint effects of two genes differs from the main effects, not only due to the traditional interaction under the nearly independent condition but the correlation between two genes. The proposed statistics are more powerful than logistic regression under various situations, cannot be affected by linkage disequilibrium and can have acceptable false positive rate as long as strictly following the reasonable GWAS data analysis roadmap. Furthermore, an application to gene pathway analysis associated with leprosy confirms in practice that our proposed gene-gene co-association concepts as well as the correspondingly proposed statistics are strongly in line with reality.     

Performance of single nucleotide polymorphisms versus haplotypes for genome-wide association analysis in barley

Genome-wide association studies (GWAS) may benefit from utilizing haplotype information for making marker-phenotype associations. Several rationales for grouping single nucleotide polymorphisms (SNPs) into haplotype blocks exist, but any advantage may depend on such factors as genetic architecture of traits, patterns of linkage disequilibrium in the study population, and marker density. The objective of this study was to explore the utility of haplotypes for GWAS in barley (Hordeum vulgare) to offer a first detailed look at this approach for identifying agronomically important genes in crops. To accomplish this, we used genotype and phenotype data from the Barley Coordinated Agricultural Project and constructed haplotypes using three different methods. Marker-trait associations were tested by the efficient mixed-model association algorithm (EMMA). When QTL were simulated using single SNPs dropped from the marker dataset, a simple sliding window performed as well or better than single SNPs or the more sophisticated methods of blocking SNPs into haplotypes. Moreover, the haplotype analyses performed better 1) when QTL were simulated as polymorphisms that arose subsequent to marker variants, and 2) in analysis of empirical heading date data. These results demonstrate that the information content of haplotypes is dependent on the particular mutational and recombinational history of the QTL and nearby markers. Analysis of the empirical data also confirmed our intuition that the distribution of QTL alleles in nature is often unlike the distribution of marker variants, and hence utilizing haplotype information could capture associations that would elude single SNPs. We recommend routine use of both single SNP and haplotype markers for GWAS to take advantage of the full information content of the genotype data.     

In-Silico Analysis of Inflammatory Bowel Disease (IBD) GWAS Loci to Novel Connections

Genome-wide association studies (GWASs) for many complex diseases, including inflammatory bowel disease (IBD), produced hundreds of disease-associated loci—the majority of which are noncoding. The number of GWAS loci is increasing very rapidly, but the process of translating single nucleotide polymorphisms (SNPs) from these loci to genomic medicine is lagging. In this study, we investigated 4,734 variants from 152 IBD associated GWAS loci (IBD associated 152 lead noncoding SNPs identified from pooled GWAS results + 4,582 variants in strong linkage-disequilibrium (LD) (r² ≥0.8) for EUR population of 1K Genomes Project) using four publicly available bioinformatics tools, e.g. dbPSHP, CADD, GWAVA, and RegulomeDB, to annotate and prioritize putative regulatory variants. Of the 152 lead noncoding SNPs, around 11% are under strong negative selection (GERP++ RS ≥2); and ~30% are under balancing selection (Tajima’s D score >2) in CEU population (1K Genomes Project)—though these regions are positively selected (GERP++ RS <0) in mammalian evolution. The analysis of 4,734 variants using three integrative annotation tools produced 929 putative functional SNPs, of which 18 SNPs (from 15 GWAS loci) are in concordance with all three classifiers. These prioritized noncoding SNPs may contribute to IBD pathogenesis by dysregulating the expression of nearby genes. This study showed the usefulness of integrative annotation for prioritizing fewer functional variants from a large number of GWAS markers.     

How Genome-Wide SNP-SNP Interactions Relate to Nasopharyngeal Carcinoma Susceptibility

This study is the first to use genome-wide association study (GWAS) data to evaluate the multidimensional genetic architecture underlying nasopharyngeal cancer. Since analysis of data from GWAS confirms a close and consistent association between elevated risk for nasopharyngeal carcinoma (NPC) and major histocompatibility complex class 1 genes, our goal here was to explore lesser effects of gene-gene interactions. We conducted an exhaustive genome-wide analysis of GWAS data of NPC, revealing two-locus interactions occurring between single nucleotide polymorphisms (SNPs), and identified a number of suggestive interaction loci which were missed by traditional GWAS analyses. Although none of the interaction pairs we identified passed the genome-wide Bonferroni-adjusted threshold for significance, using independent GWAS data from the same population (Stage 2), we selected 66 SNP pairs in 39 clusters with P<0.01. We identified that in several chromosome regions, multiple suggestive interactions group to form a block-like signal, effectively reducing the rate of false discovery. The strongest cluster of interactions involved the CREB5 gene and a SNP rs1607979 on chromosome 17q22 (P = 9.86×10⁻¹¹) which also show trans-expression quantitative loci (eQTL) association in Chinese population. We then detected a complicated cis-interaction pattern around the NPC-associated HLA-B locus, which is immediately adjacent to copy-number variations implicated in male susceptibility for NPC. While it remains to be seen exactly how and to what degree SNP-SNP interactions such as these affect susceptibility for nasopharyngeal cancer, future research on these questions holds great promise for increasing our understanding of this disease’s genetic etiology, and possibly also that of other gene-related cancers.