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.     

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.     

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.     

Adipocyte Secreted Factors Enhance Aggressiveness of Prostate Carcinoma Cells

Obesity has been associated with increased incidence and risk of mortality of prostate cancer. One of the proposed mechanisms underlying this risk association is the change in adipokines expression that could promote the development and progression of the prostate tumor cells. The main goal of this study was to evaluate the effect of preadipocyte and adipocyte secretome in the proliferation, migration and invasion of androgen independent prostate carcinoma cells (RM1) and to assess cell proliferation in the presence of the adiposity signals leptin and insulin. RM1 cells were co-cultured in with preadipocytes, adipocytes or cultured in their respective conditioned medium. Cell proliferation was assessed by flow cytometry and XTT viability test. Cell migration was evaluated using a wound healing injury assay of RM1 cells cultured with conditioned media. Cellular invasion of RM1 cells co-cultured with adipocytes and preadipocytes was assessed using matrigel membranes. Preadipocyte conditioned medium was associated with a small increase in RM1 proliferation, while adipocytes conditioned media significantly increased RM1 cell proliferation (p<0.01). Adipocytes also significantly increased the RM1 cells proliferation in co-culture (p <0.01). Cell migration was higher in RM1 cells cultured with preadipocyte and adipocyte conditioned medium. RM1 cell invasion was significantly increased after co-culture with preadipocytes and adipocytes (p <0.05). Insulin also increased significantly the cell proliferation in contrast to leptin, which showed no effect. In conclusion, prostate carcinoma cells seem to be influenced by factors secreted by adipocytes that are able to increase their ability to proliferate, migrate and invade.     

Low-Dose Bisphenol-A Impairs Adipogenesis and Generates Dysfunctional 3T3-L1 Adipocytes

Environmental endocrine disruptors (EDCs), including bisphenol-A (BPA), have been recently involved in obesity and diabetes by dysregulating adipose tissue function. Our aim was to examine whether prolonged exposure to low doses of BPA could affect adipogenesis and adipocyte metabolic functions. Therefore, 3T3-L1 pre-adipocytes were cultured for three weeks with BPA 1nM to mimic human environmental exposure. We evaluated BPA effect on cell proliferation, differentiation, gene expression and adipocyte metabolic function. BPA significantly increased pre-adipocyte proliferation (p<0.01). In 3T3-L1 adipocytes differentiated in the presence of BPA, the expression of Peroxisome proliferator-activated receptor gamma (PPARγ), Fatty Acid Binding Protein 4/Adipocyte Protein 2 (FABP4/AP2) and CCAAT/enhancer binding protein (C/EBPα) was increased by 3.5, 1.5 and 3 folds, respectively. Mature adipocytes also showed a significant increase in lipid accumulation (p<0.05) and alterations of insulin action, with significant reduction in insulin-stimulated glucose utilization (p<0.001). Moreover, in mature adipocytes, mRNA levels of Leptin, interleukin-6 (IL6) and interferon-γ (IFNγ) were significantly increased (p<0.05). In conclusion, BPA prolonged exposure at low doses, consistent with those found in the environment, may affect adipocyte differentiation program, enhancing pre-adipocyte proliferation and anticipating the expression of the master genes involved in lipid/glucose metabolism. The resulting adipocytes are hypertrophic, with impaired insulin signaling, reduced glucose utilization and increased pro-inflammatory cytokine expression. Thus, these data supported the hypothesis that BPA exposure, during critical stages of adipose tissue development, may cause adipocyte metabolic dysfunction and inflammation, thereby increasing the risk of developing obesity-related diseases.     

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.     

An Evi1-C/EBPβ complex controls peroxisome proliferator-activated receptor γ2 gene expression to initiate white fat cell differentiation

Fibroblastic preadipocyte cells are recruited to differentiate into new adipocytes during the formation and hyperplastic growth of white adipose tissue. Peroxisome proliferator-activated receptor γ (PPARγ), the master regulator of adipogenesis, is expressed at low levels in preadipocytes, and its levels increase dramatically and rapidly during the differentiation process. However, the mechanisms controlling the dynamic and selective expression of PPARγ in the adipocyte lineage remain largely unknown. We show here that the zinc finger protein Evi1 increases in preadipocytes at the onset of differentiation prior to increases in PPARγ levels. Evi1 expression converts nonadipogenic cells into adipocytes via an increase in the predifferentiation levels of PPARγ2, the adipose-selective isoform of PPARγ. Conversely, loss of Evi1 in preadipocytes blocks the induction of PPARγ2 and suppresses adipocyte differentiation. Evi1 binds with C/EBPβ to regulatory sites in the Pparγ locus at early stages of adipocyte differentiation, coincident with the induction of Pparγ2 expression. These results indicate that Evi1 is a key regulator of adipogenic competency.     

The influence of preadipocyte differentiation capacity on lipolysis in human mature adipocytes.

The ability of catecholamines to maximally stimulate adipocyte lipolysis (lipolytic capacity) is decreased in obesity. It is not known whether the lipolytic capacity is determined by the ability of adipocytes to differentiate. The aim of the study was to investigate if lipolytic capacity is related to preadipocyte differentiation and if the latter can predict lipolysis in mature adipocytes. IN VITRO experiments were performed on differentiating preadipocytes and isolated mature adipocytes from human subcutaneous adipose tissue. In preadipocytes, noradrenaline-induced lipolysis increased significantly until terminal differentiation (day 12). However, changes in the expression of genes involved in lipolysis (hormone sensitive lipase, adipocyte triglyceride lipase, the alpha2-and beta1-adrenoceptors, perilipin, and fatty acid binding protein) reached a plateau much earlier during differentiation (day 8). A significant positive correlation between lipolysis in differentiated preadipocytes and mature adipocytes was observed for noradrenaline (r=0.5, p<0.01). The late differentiation capacity of preadipocytes measured as glycerol-3-phosphate dehydrogenase activity was positively correlated with noradrenaline-induced lipolysis in preadipocytes (r=0.51, p<0.005) and mature fat cells (r=0.35, p<0.05). In conclusion, intrinsic properties related to terminal differentiation determine the ability of catecholamines to maximally stimulate lipolysis in fat cells. The inability to undergo full differentiation might in part explain the low lipolytic capacity of fat cells among the obese.     

Systematic Genetic Analysis Identifies Cis-eQTL Target Genes Associated with Glioblastoma Patient Survival

Prior expression quantitative trait locus (eQTL) studies have demonstrated heritable variation determining differences in gene expression. The majority of eQTL studies were based on cell lines and normal tissues. We performed cis-eQTL analysis using glioblastoma multiforme (GBM) data sets obtained from The Cancer Genome Atlas (TCGA) to systematically investigate germline variation’s contribution to tumor gene expression levels. We identified 985 significant cis-eQTL associations (FDR<0.05) mapped to 978 SNP loci and 159 unique genes. Approximately 57% of these eQTLs have been previously linked to the gene expression in cell lines and normal tissues; 43% of these share cis associations known to be associated with functional annotations. About 25% of these cis-eQTL associations are also common to those identified in Breast Cancer from a recent study. Further investigation of the relationship between gene expression and patient clinical information identified 13 eQTL genes whose expression level significantly correlates with GBM patient survival (p<0.05). Most of these genes are also differentially expressed in tumor samples and organ-specific controls (p<0.05). Our results demonstrated a significant relationship of germline variation with gene expression levels in GBM. The identification of eQTLs-based expression associated survival might be important to the understanding of genetic contribution to GBM cancer prognosis.     

Re-sequencing Expands Our Understanding of the Phenotypic Impact of Variants at GWAS Loci

Genome-wide association studies (GWAS) have identified >500 common variants associated with quantitative metabolic traits, but in aggregate such variants explain at most 20–30% of the heritable component of population variation in these traits. To further investigate the impact of genotypic variation on metabolic traits, we conducted re-sequencing studies in >6,000 members of a Finnish population cohort (The Northern Finland Birth Cohort of 1966 [NFBC]) and a type 2 diabetes case-control sample (The Finland-United States Investigation of NIDDM Genetics [FUSION] study). By sequencing the coding sequence and 5′ and 3′ untranslated regions of 78 genes at 17 GWAS loci associated with one or more of six metabolic traits (serum levels of fasting HDL-C, LDL-C, total cholesterol, triglycerides, plasma glucose, and insulin), and conducting both single-variant and gene-level association tests, we obtained a more complete understanding of phenotype-genotype associations at eight of these loci. At all eight of these loci, the identification of new associations provides significant evidence for multiple genetic signals to one or more phenotypes, and at two loci, in the genes ABCA1 and CETP, we found significant gene-level evidence of association to non-synonymous variants with MAF<1%. Additionally, two potentially deleterious variants that demonstrated significant associations (rs138726309, a missense variant in G6PC2, and rs28933094, a missense variant in LIPC) were considerably more common in these Finnish samples than in European reference populations, supporting our prior hypothesis that deleterious variants could attain high frequencies in this isolated population, likely due to the effects of population bottlenecks. Our results highlight the value of large, well-phenotyped samples for rare-variant association analysis, and the challenge of evaluating the phenotypic impact of such variants.     

Trans-Ethnic Fine-Mapping of Lipid Loci Identifies Population-Specific Signals and Allelic Heterogeneity That Increases the Trait Variance Explained

Genome-wide association studies (GWAS) have identified ∼100 loci associated with blood lipid levels, but much of the trait heritability remains unexplained, and at most loci the identities of the trait-influencing variants remain unknown. We conducted a trans-ethnic fine-mapping study at 18, 22, and 18 GWAS loci on the Metabochip for their association with triglycerides (TG), high-density lipoprotein cholesterol (HDL-C), and low-density lipoprotein cholesterol (LDL-C), respectively, in individuals of African American (n = 6,832), East Asian (n = 9,449), and European (n = 10,829) ancestry. We aimed to identify the variants with strongest association at each locus, identify additional and population-specific signals, refine association signals, and assess the relative significance of previously described functional variants. Among the 58 loci, 33 exhibited evidence of association at P<1×10⁻⁴ in at least one ancestry group. Sequential conditional analyses revealed that ten, nine, and four loci in African Americans, Europeans, and East Asians, respectively, exhibited two or more signals. At these loci, accounting for all signals led to a 1.3- to 1.8-fold increase in the explained phenotypic variance compared to the strongest signals. Distinct signals across ancestry groups were identified at PCSK9 and APOA5. Trans-ethnic analyses narrowed the signals to smaller sets of variants at GCKR, PPP1R3B, ABO, LCAT, and ABCA1. Of 27 variants reported previously to have functional effects, 74% exhibited the strongest association at the respective signal. In conclusion, trans-ethnic high-density genotyping and analysis confirm the presence of allelic heterogeneity, allow the identification of population-specific variants, and limit the number of candidate SNPs for functional studies.