Integrative eQTL-based analyses reveal the biology of breast cancer risk loci

An argument for mechanism-based statistical inference in cancer

Cancer is perhaps the prototypical systems disease, and as such has been the focus of extensive study in quantitative systems biology. However, translating these programs into personalized clinical care remains elusive and incomplete. In this perspective, we argue that realizing this agenda—in particular, predicting disease phenotypes, progression and treatment response for individuals—requires going well beyond standard computational and bioinformatics tools and algorithms. It entails designing global mathematical models over network-scale configurations of genomic states and molecular concentrations, and learning the model parameters from limited available samples of high-dimensional and integrative omics data. As such, any plausible design should accommodate: biological mechanism, necessary for both feasible learning and interpretable decision making; stochasticity, to deal with uncertainty and observed variation at many scales; and a capacity for statistical inference at the patient level. This program, which requires a close, sustained collaboration between mathematicians and biologists, is illustrated in several contexts, including learning biomarkers, metabolism, cell signaling, network inference and tumorigenesis.

The 12p13.33/RAD52 locus and genetic susceptibility to squamous cell cancers of upper aerodigestive tract

Genetic variants located within the 12p13.33/RAD52 locus have been associated with lung squamous cell carcinoma (LUSC). Here, within 5,947 UADT cancers and 7,789 controls from 9 different studies, we found rs10849605, a common intronic variant in RAD52, to be also associated with upper aerodigestive tract (UADT) squamous cell carcinoma cases (OR = 1.09, 95% CI: 1.04-1.15, p = 6x10(-4)). We additionally identified rs10849605 as a RAD52 cis-eQTL inUADT(p = 1x10(-3)) and LUSC (p = 9x10(-4)) tumours, with the UADT/LUSC risk allele correlated with increased RAD52 expression levels. The 12p13.33 locus, encompassing rs10849605/RAD52, was identified as a significant somatic focal copy number amplification in UADT(n = 374, q-value = 0.075) and LUSC (n = 464, q-value = 0.007) tumors and correlated with higher RAD52 tumor expression levels (p = 6x10(-48) and p = 3x10(-29) in UADT and LUSC, respectively). In combination, these results implicate increased RAD52 expression in both genetic susceptibility and tumorigenesis of UADT and LUSC tumors.

An integrated network of microRNA and gene expression in ovarian cancer

Background

Ovarian cancer is a deadly female reproductive cancer. Understanding the biological mechanisms underlying ovarian cancer could help lead to quicker and more accurate diagnosis and more effective treatments. Both changes in microRNA(miRNA) expression and miRNA/mRNA dysregulation have been associated with ovarian cancer. With the availability of whole-genome miRNA and mRNA sequencing we now have new potentials to study these associations. In this study, we performed a comprehensive analysis of miRNA and mRNA expression in ovarian cancer using an integrative network approach combined with association analysis.

Results

We developed an integrative approach to construct a network that illustrates the complex interplay among miRNA and gene expression from a systems perspective. Our method is composed of expanding networks from eQTL associations, building network associations in eQTL analysis, and then combine the networks into an integrated network. This integrated network takes account of miRNA expression quantitative trait loci (eQTL) associations, miRNAs and their targets, protein-protein interactions, co-expressions among miRNAs and genes respectively. Applied to the ovarian cancer data set from The Cancer Genome Atlas (TCGA), we created an integrated network with 167 nodes containing 108 miRNA-target interactions and 145 from protein-protein interactions, starting from 44 initial eQTLs. This integrated network encompassed 26 genes and 14 miRNAs associated with cancer. In particular, 11 genes and 12 miRNAs in the integrated network are associated with ovarian cancer.

Conclusion

We demonstrated an integrated network approach that integrates multiple data sources at a systems level. We applied this approach to the TCGA ovarian cancer dataset, and constructed a network that provided a more inclusive view of miRNA and gene expression in ovarian cancer. This network included four separate types of interactions among miRNAs and genes. Simply analyzing each interaction component in isolation, such as the eQTL associations, the miRNA-target interactions or the protein-protein interactions, would create a much more limited network than the integrated one.

In-silico identification and functional validation of allele-dependent AR enhancers

Androgen Receptor (AR) and Estrogen Receptors (ERs) are key nuclear receptors that can cooperate in orchestrating gene expression programs in multiple tissues and diseases, targeting binding elements in promoters and distant enhancers. We report the unbiased identification of enhancer elements bound by AR and ER-α whose activity can be allele-specific depending on the status of nearby Single Nucleotide Polymorphisms (SNP). ENCODE data were computationally mined to nominate genomic loci with: (i) chromatin signature of enhancer activity from activation histone marks, (ii) binding evidence by AR and ER-α, (iii) presence of a SNP. Forty-one loci were identified and two, on 1q21.3 and 13q34, selected for characterization by gene reporter, Chromatin immunoprecipitation (ChIP) and RT-qPCR assays in breast (MCF7) and prostate (PC-3) cancer-derived cell lines. We observed allele-specific enhancer activity, responsiveness to ligand-bound AR, and potentially influence on the transcription of closely located genes (RAB20, ING1, ARHGEF7, ADAM15). The 1q21.3 variant, rs2242193, showed impact on AR binding in MCF7 cells that are heterozygous for the SNP. Our unbiased genome-wide search proved to be an efficient methodology to discover new functional polymorphic regulatory regions (PRR) potentially acting as risk modifiers in hormone-driven cancers and overall nominated SNPs in PRR across 136 transcription factors.

Serum microRNA expression patterns that predict early treatment failure in prostate cancer patients

We aimed to identify microRNA (miRNA) expression patterns in the serum of prostate cancer (CaP) patients that predict the risk of early treatment failure following radical prostatectomy (RP). Microarray and Q-RT-PCR analyses identified 43 miRNAs as differentiating disease stages within 14 prostate cell lines and reflectedpublically available patient data. 34 of these miRNA were detectable in the serum of CaP patients. Association with time to biochemical progression was examined in a cohort of CaP patients following RP. A greater than two-fold increase in hazard of biochemical progression associated with altered expression of miR-103, miR-125b and miR-222 (p <.0008) in the serum of CaP patients. Prediction models based on penalized regression analyses showed that the levels of the miRNAs and PSA together were better at detecting false positives than models without miRNAs, for similar level of sensitivity. Analyses of publically available data revealed significant and reciprocal relationships between changes in CpG methylation and miRNA expression patterns suggesting a role for CpG methylation to regulate miRNA. Exploratory validation supported roles for miR-222 and miR-125b to predict progression risk in CaP. The current study established that expression patterns of serum-detectable miRNAs taken at the time of RP are prognostic for men who are at risk of experiencing subsequent early biochemical progression. These non-invasive approaches could be used to augment treatment decisions.

Mediation analysis demonstrates that trans-eQTLs are often explained by cis-mediation: a genome-wide analysis among 1,800 South Asians

A large fraction of human genes are regulated by genetic variation near the transcribed sequence (cis-eQTL, expression quantitative trait locus), and many cis-eQTLs have implications for human disease. Less is known regarding the effects of genetic variation on expression of distant genes (trans-eQTLs) and their biological mechanisms. In this work, we use genome-wide data on SNPs and array-based expression measures from mononuclear cells obtained from a population-based cohort of 1,799 Bangladeshi individuals to characterize cis- and trans-eQTLs and determine if observed trans-eQTL associations are mediated by expression of transcripts in cis with the SNPs showing trans-association, using Sobel tests of mediation. We observed 434 independent trans-eQTL associations at a false-discovery rate of 0.05, and 189 of these trans-eQTLs were also cis-eQTLs (enrichment P<0.0001). Among these 189 trans-eQTL associations, 39 were significantly attenuated after adjusting for a cis-mediator based on Sobel P<10^-5. We attempted to replicate 21 of these mediation signals in two European cohorts, and while only 7 trans-eQTL associations were present in one or both cohorts, 6 showed evidence of cis-mediation. Analyses of simulated data show that complete mediation will be observed as partial mediation in the presence of mediator measurement error or imperfect LD between measured and causal variants. Our data demonstrates that trans-associations can become significantly stronger or switch directions after adjusting for a potential mediator. Using simulated data, we demonstrate that this phenomenon is expected in the presence of strong cis-trans confounding and when the measured cis-transcript is correlated with the true (unmeasured) mediator. In conclusion, by applying mediation analysis to eQTL data, we show that a substantial fraction of observed trans-eQTL associations can be explained by cis-mediation. Future studies should focus on understanding the mechanisms underlying widespread cis-mediation and their relevance to disease biology, as well as using mediation analysis to improve eQTL discovery.

Expression quantitative trait locus (eQTL) studies have demonstrated that human genes can be regulated by genetic variation residing close to the gene (cis-eQTLs) or in a distant region or on a different chromosome (trans-eQTLs). While cis-eQTL variants are likely to affect transcription factor binding or chromatin structure, our understanding of the mechanisms underlying trans-eQTLs is incomplete. We hypothesize that a substantial fraction of trans-eQTLs influence expression of distant genes through mediation by expression levels of a cis-transcript. In this paper, we use genome-wide SNPs and expression data for 1,799 South Asians to identify cis- and trans-eQTLs and to test our hypothesis using Sobel tests of mediation. Among 189 observed trans-eQTL associations, we provide evidence of cis-mediation for 39, 6 of which show mediation in an independent European cohort. We used simulated data to demonstrate that complete mediation will be observed as partial mediation in the presence of mediator measurement error or imperfect LD between measured and causal variants. We also demonstrate how unobserved confounding variables and incorrect mediator selection can bias mediation estimates. In conclusion, we have identified cis-mediators for many trans-eQTLs and described a mediation analysis approach that can be used to validate, characterize, and enhance discovery of trans-eQTLs.

TEGS-CN: A Statistical Method for Pathway Analysis of Genome-wide Copy Number Profile

The effects of copy number alterations make up a significant part of the tumor genome profile, but pathway analyses of these alterations are still not well established. We proposed a novel method to analyze multiple copy numbers of genes within a pathway, termed Test for the Effect of a Gene Set with Copy Number data (TEGS-CN). TEGS-CN was adapted from TEGS, a method that we previously developed for gene expression data using a variance component score test. With additional development, we extend the method to analyze DNA copy number data, accounting for different sizes and thus various numbers of copy number probes in genes. The test statistic follows a mixture of X² distributions that can be obtained using permutation with scaled X² approximation. We conducted simulation studies to evaluate the size and the power of TEGS-CN and to compare its performance with TEGS. We analyzed a genome-wide copy number data from 264 patients of non-small-cell lung cancer. With the Molecular Signatures Database (MSigDB) pathway database, the genome-wide copy number data can be classified into 1814 biological pathways or gene sets. We investigated associations of the copy number profile of the 1814 gene sets with pack-years of cigarette smoking. Our analysis revealed five pathways with significant P values after Bonferroni adjustment (<2.8 × 10⁻⁵), including the PTEN pathway (7.8 × 10⁻⁷), the gene set up-regulated under heat shock (3.6 × 10⁻⁶), the gene sets involved in the immune profile for rejection of kidney transplantation (9.2 × 10⁻⁶) and for transcriptional control of leukocytes (2.2 × 10⁻⁵), and the ganglioside biosynthesis pathway (2.7 × 10⁻⁵). In conclusion, we present a new method for pathway analyses of copy number data, and causal mechanisms of the five pathways require further study.

Genetic variation in mitotic regulatory pathway genes is associated with breast tumor grade

Mitotic index is an important component of histologic grade and has an etiologic role in breast tumorigenesis. Several small candidate gene studies have reported associations between variation in mitotic genes and breast cancer risk. We measured associations between 2156 single nucleotide polymorphisms (SNPs) from 194 mitotic genes and breast cancer risk, overall and by histologic grade, in the Breast Cancer Association Consortium (BCAC) iCOGS study (n = 39 067 cases; n = 42 106 controls). SNPs in TACC2 [rs17550038: odds ratio (OR) = 1.24, 95% confidence interval (CI) 1.16-1.33, P = 4.2 × 10(-10)) and EIF3H (rs799890: OR = 1.07, 95% CI 1.04-1.11, P = 8.7 × 10(-6)) were significantly associated with risk of low-grade breast cancer. The TACC2 signal was retained (rs17550038: OR = 1.15, 95% CI 1.07-1.23, P = 7.9 × 10(-5)) after adjustment for breast cancer risk SNPs in the nearby FGFR2 gene, suggesting that TACC2 is a novel, independent genome-wide significant genetic risk locus for low-grade breast cancer. While no SNPs were individually associated with high-grade disease, a pathway-level gene set analysis showed that variation across the 194 mitotic genes was associated with high-grade breast cancer risk (P = 2.1 × 10(-3)). These observations will provide insight into the contribution of mitotic defects to histological grade and the etiology of breast cancer.

Systematic analysis of noncoding somatic mutations and gene expression alterations across 14 tumor types

Somatic mutations in noncoding sequences are poorly explored in cancer, a rare exception being the recent identification of activating mutations in TERT regulatory DNA. Although this finding is suggestive of a general mechanism for oncogene activation, this hypothesis remains untested. Here we map somatic mutations in 505 tumor genomes across 14 cancer types and systematically screen for associations between mutations in regulatory regions and RNA-level changes. We identify recurrent promoter mutations in several genes but find that TERT mutations are exceptional in showing a strong and genome-wide significant association with increased expression. Detailed analysis of TERT across cancers shows that the strength of this association is highly variable and is strongest in copy number-stable cancers such as thyroid carcinoma. We additionally propose that TERT promoter mutations control expression of the nearby gene CLPTM1L. Our analysis provides a detailed pan-cancer view of TERT transcriptional activation but finds no clear evidence for frequent oncogenic promoter mutations beyond TERT.

Unbiased analysis of potential targets of breast cancer susceptibility loci by Capture Hi-C

Genome-wide association studies have identified more than 70 common variants that are associated with breast cancer risk. Most of these variants map to non-protein-coding regions and several map to gene deserts, regions of several hundred kilobases lacking protein-coding genes. We hypothesized that gene deserts harbor long-range regulatory elements that can physically interact with target genes to influence their expression. To test this, we developed Capture Hi-C (CHi-C), which, by incorporating a sequence capture step into a Hi-C protocol, allows high-resolution analysis of targeted regions of the genome. We used CHi-C to investigate long-range interactions at three breast cancer gene deserts mapping to 2q35, 8q24.21, and 9q31.2. We identified interaction peaks between putative regulatory elements ("bait fragments") within the captured regions and "targets" that included both protein-coding genes and long noncoding (lnc) RNAs over distances of 6.6 kb to 2.6 Mb. Target protein-coding genes were IGFBP5, KLF4, NSMCE2, and MYC; and target lncRNAs included DIRC3, PVT1, and CCDC26. For one gene desert, we were able to define two SNPs (rs12613955 and rs4442975) that were highly correlated with the published risk variant and that mapped within the bait end of an interaction peak. In vivo ChIP-qPCR data show that one of these, rs4442975, affects the binding of FOXA1 and implicate this SNP as a putative functional variant.

Pharmacogenetic meta-analysis of genome-wide association studies of LDL cholesterol response to statins

Statins effectively lower LDL cholesterol levels in large studies and the observed interindividual response variability may be partially explained by genetic variation. Here we perform a pharmacogenetic meta-analysis of genome-wide association studies (GWAS) in studies addressing the LDL cholesterol response to statins, including up to 18,596 statin-treated subjects. We validate the most promising signals in a further 22,318 statin recipients and identify two loci, SORT1/CELSR2/PSRC1 and SLCO1B1, not previously identified in GWAS. Moreover, we confirm the previously described associations with APOE and LPA. Our findings advance the understanding of the pharmacogenetic architecture of statin response.

Parent-of-origin-specific allelic associations among 106 genomic loci for age at menarche

Age at menarche is a marker of timing of puberty in females. It varies widely between individuals, is a heritable trait and is associated with risks for obesity, type 2 diabetes, cardiovascular disease, breast cancer and all-cause mortality. Studies of rare human disorders of puberty and animal models point to a complex hypothalamic-pituitary-hormonal regulation, but the mechanisms that determine pubertal timing and underlie its links to disease risk remain unclear. Here, using genome-wide and custom-genotyping arrays in up to 182,416 women of European descent from 57 studies, we found robust evidence (P < 5 × 10(-8)) for 123 signals at 106 genomic loci associated with age at menarche. Many loci were associated with other pubertal traits in both sexes, and there was substantial overlap with genes implicated in body mass index and various diseases, including rare disorders of puberty. Menarche signals were enriched in imprinted regions, with three loci (DLK1-WDR25, MKRN3-MAGEL2 and KCNK9) demonstrating parent-of-origin-specific associations concordant with known parental expression patterns. Pathway analyses implicated nuclear hormone receptors, particularly retinoic acid and γ-aminobutyric acid-B2 receptor signalling, among novel mechanisms that regulate pubertal timing in humans. Our findings suggest a genetic architecture involving at least hundreds of common variants in the coordinated timing of the pubertal transition.

A meta-analysis of 87,040 individuals identifies 23 new susceptibility loci for prostate cancer

Genome-wide association studies (GWAS) have identified 76 variants associated with prostate cancer risk predominantly in populations of European ancestry. To identify additional susceptibility loci for this common cancer, we conducted a meta-analysis of > 10 million SNPs in 43,303 prostate cancer cases and 43,737 controls from studies in populations of European, African, Japanese and Latino ancestry. Twenty-three new susceptibility loci were identified at association P < 5 × 10(-8); 15 variants were identified among men of European ancestry, 7 were identified in multi-ancestry analyses and 1 was associated with early-onset prostate cancer. These 23 variants, in combination with known prostate cancer risk variants, explain 33% of the familial risk for this disease in European-ancestry populations. These findings provide new regions for investigation into the pathogenesis of prostate cancer and demonstrate the usefulness of combining ancestrally diverse populations to discover risk loci for disease.

Expression QTL-based analyses reveal candidate causal genes and loci across five tumor types

The majority of trait-associated loci discovered through genome-wide association studies are located outside of known protein coding regions. Consequently, it is difficult to ascertain the mechanism underlying these variants and to pinpoint the causal alleles. Expression quantitative trait loci (eQTLs) provide an organizing principle to address both of these issues. eQTLs are genetic loci that correlate with RNA transcript levels. Large-scale data sets such as the Cancer Genome Atlas (TCGA) provide an ideal opportunity to systematically evaluate eQTLs as they have generated multiple data types on hundreds of samples. We evaluated the determinants of gene expression (germline variants and somatic copy number and methylation) and performed cis-eQTL analyses for mRNA expression and miRNA expression in five tumor types (breast, colon, kidney, lung and prostate). We next tested 149 known cancer risk loci for eQTL effects, and observed that 42 (28.2%) were significantly associated with at least one transcript. Lastly, we described a fine-mapping strategy for these 42 eQTL target-gene associations based on an integrated strategy that combines the eQTL level of significance and the regulatory potential as measured by DNaseI hypersensitivity. For each of the risk loci, our analyses suggested 1 to 81 candidate causal variants that may be prioritized for downstream functional analysis. In summary, our study provided a comprehensive landscape of the genetic determinants of gene expression in different tumor types and ranked the genes and loci for further functional assessment of known cancer risk loci.

Functional annotation of colon cancer risk SNPs

Colorectal cancer (CRC) is a leading cause of cancer-related deaths in the United States. Genome-wide association studies (GWAS) have identified single nucleotide polymorphisms (SNPs) associated with increased risk for CRC. A molecular understanding of the functional consequences of this genetic variation has been complicated because each GWAS SNP is a surrogate for hundreds of other SNPs, most of which are located in non-coding regions. Here we use genomic and epigenomic information to test the hypothesis that the GWAS SNPs and/or correlated SNPs are in elements that regulate gene expression, and identify 23 promoters and 28 enhancers. Using gene expression data from normal and tumour cells, we identify 66 putative target genes of the risk-associated enhancers (10 of which were also identified by promoter SNPs). Employing CRISPR nucleases, we delete one risk-associated enhancer and identify genes showing altered expression. We suggest that similar studies be performed to characterize all CRC risk-associated enhancers.

Previous studies identified genetic variants associated with colorectal cancer (CRC), but the functional consequences of these genetic risk factors remain poorly understood. Here, the authors report that CRC risk variants reside in promoters and enhancers and could increase colon cancer risk through gene expression regulation.

Genetic polymorphisms in the 9p21 region associated with risk of multiple cancers

The chromosome 9p21 region has been implicated in the pathogenesis of multiple cancers. We analyzed 9p21 single nucleotide polymorphisms (SNPs) from eight genome-wide association studies (GWAS) with data deposited in dbGaP, including studies of esophageal squamous cell carcinoma (ESCC), gastric cancer (GC), pancreatic cancer, renal cell carcinoma (RCC), lung cancer (LC), breast cancer (BrC), bladder cancer (BC) and prostate cancer (PrC). The number of subjects ranged from 2252 (PrC) to 7619 (LC). SNP-level analyses for each cancer were conducted by logistic regression or random-effects meta-analysis. A subset-based statistical approach (ASSET) was performed to combine SNP-level P values across multiple cancers. We calculated gene-level P values using the adaptive rank truncated product method. We identified that rs1063192 and rs2157719 in the CDKN2A/2B region were significantly associated with ESCC and rs2764736 (3' of TUSC1) was associated with BC (P ≤ 2.59 × 10(-6)). ASSET analyses identified four SNPs significantly associated with multiple cancers: rs3731239 (CDKN2A intronic) with ESCC, GC and BC (P = 3.96 × 10(-) (4)); rs10811474 (3' of IFNW1) with RCC and BrC (P = 0.001); rs12683422 (LINGO2 intronic) with RCC and BC (P = 5.93 × 10(-) (4)) and rs10511729 (3' of ELAVL2) with LC and BrC (P = 8.63 × 10(-) (4)). At gene level, CDKN2B, CDKN2A and CDKN2B-AS1 were significantly associated with ESCC (P ≤ 4.70 × 10(-) (5)). Rs10511729 and rs10811474 were associated with cis-expression of 9p21 genes in corresponding cancer tissues in the expression quantitative trait loci analysis. In conclusion, we identified several genetic variants in the 9p21 region associated with the risk of multiple cancers, suggesting that this region may contribute to a shared susceptibility across different cancer types.

Expression QTL-based analyses reveal the mechanisms underlying colorectal cancer predisposition

Genome-wide association studies have identified many risk loci associated with colorectal cancer. Strategies integrating biological data sets with GWAS results will provide insights into the roles of risk single-nucleotide polymorphisms. We performed expression quantitative trait locus-based analyses using the information provided in The Cancer Genome Atlas. Analysis of the cis-expression quantitative trait loci (eQTLs) of 18 previously reported colorectal cancer risk loci resulted in the discovery of five variants that were significantly associated with gene expressions. Analysis of the trans-eQTLs identified three risk loci that affect the expression levels of a neighboring transcription factor, MYC. These findings provide a more comprehensive picture of gene expression determinants in colorectal cancer and insights into the underlying biology of risk loci.

Prostate cancer risk locus at 8q24 as a regulatory hub by physical interactions with multiple genomic loci across the genome

Chromosome 8q24 locus contains regulatory variants that modulate genetic risk to various cancers including prostate cancer (PC). However, the biological mechanism underlying this regulation is not well understood. Here, we developed a chromosome conformation capture (3C)-based multi-target sequencing technology and systematically examined three PC risk regions at the 8q24 locus and their potential regulatory targets across human genome in six cell lines. We observed frequent physical contacts of this risk locus with multiple genomic regions, in particular, inter-chromosomal interaction with CD96 at 3q13 and intra-chromosomal interaction with MYC at 8q24. We identified at least five interaction hot spots within the predicted functional regulatory elements at the 8q24 risk locus. We also found intra-chromosomal interaction genes PVT1, FAM84B and GSDMC and inter-chromosomal interaction gene CXorf36 in most of the six cell lines. Other gene regions appeared to be cell line-specific, such as RRP12 in LNCaP, USP14 in DU-145 and SMIN3 in lymphoblastoid cell line. We further found that the 8q24 functional domains more likely interacted with genomic regions containing genes enriched in critical pathways such as Wnt signaling and promoter motifs such as E2F1 and TCF3. This result suggests that the risk locus may function as a regulatory hub by physical interactions with multiple genes important for prostate carcinogenesis. Further understanding genetic effect and biological mechanism of these chromatin interactions will shed light on the newly discovered regulatory role of the risk locus in PC etiology and progression.

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.

Metastasis-associated protein ribosomal RNA processing 1 homolog B (RRP1B) modulates metastasis through regulation of histone methylation

Overexpression of ribosomal RNA processing 1 homolog B (RRP1B) induces a transcriptional profile that accurately predicts patient outcome in breast cancer. However, the mechanism by which RRP1B modulates transcription is unclear. Here, the chromatin-binding properties of RRP1B were examined to define how it regulates metastasis-associated transcription. To identify genome-wide RRP1B-binding sites, high-throughput ChIP-seq was performed in the human breast cancer cell line MDA-MB-231 and HeLa cells using antibodies against endogenous RRP1B. Global changes in repressive marks such as histone H3 lysine 9 trimethylation (H3K9me3) were also examined by ChIP-seq. Analysis of these samples identified 339 binding regions in MDA-MB-231 cells and 689 RRP1B-binding regions in HeLa cells. Among these, 136 regions were common to both cell lines. Gene expression analyses of these RRP1B-binding regions revealed that transcriptional repression is the primary result of RRP1B binding to chromatin. ChIP-reChIP assays demonstrated that RRP1B co-occupies loci with decreased gene expression with the heterochromatin-associated proteins, tripartite motif-containing protein 28 (TRIM28/KAP1), and heterochromatin protein 1-α (CBX5/HP1α). RRP1B occupancy at these loci was also associated with higher H3K9me3 levels, indicative of heterochromatinization mediated by the TRIM28/HP1α complex. In addition, RRP1B upregulation, which is associated with metastasis suppression, induced global changes in histone methylation.

IMPLICATIONS

RRP1B, a breast cancer metastasis suppressor, regulates gene expression through heterochromatinization and transcriptional repression, which helps our understanding of mechanisms that drive prognostic gene expression in human breast cancer.

Effects of long-term averaging of quantitative blood pressure traits on the detection of genetic associations

Blood pressure (BP) is a heritable, quantitative trait with intraindividual variability and susceptibility to measurement error. Genetic studies of BP generally use single-visit measurements and thus cannot remove variability occurring over months or years. We leveraged the idea that averaging BP measured across time would improve phenotypic accuracy and thereby increase statistical power to detect genetic associations. We studied systolic BP (SBP), diastolic BP (DBP), mean arterial pressure (MAP), and pulse pressure (PP) averaged over multiple years in 46,629 individuals of European ancestry. We identified 39 trait-variant associations across 19 independent loci (p < 5 × 10(-8)); five associations (in four loci) uniquely identified by our LTA analyses included those of SBP and MAP at 2p23 (rs1275988, near KCNK3), DBP at 2q11.2 (rs7599598, in FER1L5), and PP at 6p21 (rs10948071, near CRIP3) and 7p13 (rs2949837, near IGFBP3). Replication analyses conducted in cohorts with single-visit BP data showed positive replication of associations and a nominal association (p < 0.05). We estimated a 20% gain in statistical power with long-term average (LTA) as compared to single-visit BP association studies. Using LTA analysis, we identified genetic loci influencing BP. LTA might be one way of increasing the power of genetic associations for continuous traits in extant samples for other phenotypes that are measured serially over time.

Molecular mechanisms of diabetic kidney disease

Diabetic kidney disease (DKD) is the leading cause of kidney failure worldwide and the single strongest predictor of mortality in patients with diabetes. DKD is a prototypical disease of gene and environmental interactions. Tight glucose control significantly decreases DKD incidence, indicating that hyperglycemia-induced metabolic alterations, including changes in energy utilization and mitochondrial dysfunction, play critical roles in disease initiation. Blood pressure control, especially with medications that inhibit the angiotensin system, is the only effective way to slow disease progression. While DKD is considered a microvascular complication of diabetes, growing evidence indicates that podocyte loss and epithelial dysfunction play important roles. Inflammation, cell hypertrophy, and dedifferentiation by the activation of classic pathways of regeneration further contribute to disease progression. Concerted clinical and basic research efforts will be needed to understand DKD pathogenesis and to identify novel drug targets.

Genomics meets proteomics: identifying the culprits in disease

Genome-wide association studies (GWAS) revealed genomic risk loci that potentially have an impact on disease and phenotypic traits. This extensive resource holds great promise in providing novel directions for personalized medicine, including disease risk prediction, prevention and targeted medication. One of the major challenges that researchers face on the path between the initial identification of an association and precision treatment of patients is the comprehension of the biological mechanisms that underlie these associations. Currently, the focus to solve these questions lies on the integrative analysis of system-wide data on global genome variation, gene expression, transcription factor binding, epigenetic profiles and chromatin conformation. The generation of this data mainly relies on next-generation sequencing. However, due to multiple recent developments, mass spectrometry-based proteomics now offers additional, by the GWAS field so far hardly recognized possibilities for the identification of functional genome variants and, in particular, for the identification and characterization of (differentially) bound protein complexes as well as physiological target genes. In this review, we introduce these proteomics advances and suggest how they might be integrated in post-GWAS workflows. We argue that the combination of highly complementary techniques is powerful and can provide an unbiased, detailed picture of GWAS loci and their mechanistic involvement in disease.

A symbiotic liaison between the genetic and epigenetic code

With rapid advances in sequencing technologies, we are undergoing a paradigm shift from hypothesis- to data-driven research. Genome-wide profiling efforts have given informative insights into biological processes; however, considering the wealth of variation, the major challenge still remains in their meaningful interpretation. In particular sequence variation in non-coding contexts is often challenging to interpret. Here, data integration approaches for the identification of functional genetic variability represent a possible solution. Exemplary, functional linkage analysis integrating genotype and expression data determined regulatory quantitative trait loci and proposed causal relationships. In addition to gene expression, epigenetic regulation and specifically DNA methylation was established as highly valuable surrogate mark for functional variance of the genetic code. Epigenetic modification has served as powerful mediator trait to elucidate mechanisms forming phenotypes in health and disease. Particularly, integrative studies of genetic and DNA methylation data have been able to guide interpretation strategies of risk genotypes, but also proved their value for physiological traits, such as natural human variation and aging. This Review seeks to illustrate the power of data integration in the genomic era exemplified by DNA methylation quantitative trait loci. However, the model is further extendable to virtually all traceable molecular traits.

Single nucleotide variants in transcription factors associate more tightly with phenotype than with gene expression

Mapping the polymorphisms responsible for variation in gene expression, known as Expression Quantitative Trait Loci (eQTL), is a common strategy for investigating the molecular basis of disease. Despite numerous eQTL studies, the relationship between the explanatory power of variants on gene expression versus their power to explain ultimate phenotypes remains to be clarified. We addressed this question using four naturally occurring Quantitative Trait Nucleotides (QTN) in three transcription factors that affect sporulation efficiency in wild strains of the yeast, Saccharomyces cerevisiae. We compared the ability of these QTN to explain the variation in both gene expression and sporulation efficiency. We find that the amount of gene expression variation explained by the sporulation QTN is not predictive of the amount of phenotypic variation explained. The QTN are responsible for 98% of the phenotypic variation in our strains but the median gene expression variation explained is only 49%. The alleles that are responsible for most of the variation in sporulation efficiency do not explain most of the variation in gene expression. The balance between the main effects and gene-gene interactions on gene expression variation is not the same as on sporulation efficiency. Finally, we show that nucleotide variants in the same transcription factor explain the expression variation of different sets of target genes depending on whether the variant alters the level or activity of the transcription factor. Our results suggest that a subset of gene expression changes may be more predictive of ultimate phenotypes than the number of genes affected or the total fraction of variation in gene expression variation explained by causative variants, and that the downstream phenotype is buffered against variation in the gene expression network.

Applying genome-wide gene-based expression quantitative trait locus mapping to study population ancestry and pharmacogenetics

BACKGROUND

Gene-based analysis has become popular in genomic research because of its appealing biological and statistical properties compared with those of a single-locus analysis. However, only a few, if any, studies have discussed a mapping of expression quantitative trait loci (eQTL) in a gene-based framework. Neither study has discussed ancestry-informative eQTL nor investigated their roles in pharmacogenetics by integrating single nucleotide polymorphism (SNP)-based eQTL (s-eQTL) and gene-based eQTL (g-eQTL).

RESULTS

In this g-eQTL mapping study, the transcript expression levels of genes (transcript-level genes; T-genes) were correlated with the SNPs of genes (sequence-level genes; S-genes) by using a method of gene-based partial least squares (PLS). Ancestry-informative transcripts were identified using a rank-score-based multivariate association test, and ancestry-informative eQTL were identified using Fisher's exact test. Furthermore, key ancestry-predictive eQTL were selected in a flexible discriminant analysis. We analyzed SNPs and gene expression of 210 independent people of African-, Asian- and European-descent. We identified numerous cis- and trans-acting g-eQTL and s-eQTL for each population by using PLS. We observed ancestry information enriched in eQTL. Furthermore, we identified 2 ancestry-informative eQTL associated with adverse drug reactions and/or drug response. Rs1045642, located on MDR1, is an ancestry-informative eQTL (P = 2.13E-13, using Fisher's exact test) associated with adverse drug reactions to amitriptyline and nortriptyline and drug responses to morphine. Rs20455, located in KIF6, is an ancestry-informative eQTL (P = 2.76E-23, using Fisher's exact test) associated with the response to statin drugs (e.g., pravastatin and atorvastatin). The ancestry-informative eQTL of drug biotransformation genes were also observed; cross-population cis-acting expression regulators included SPG7, TAP2, SLC7A7, and CYP4F2. Finally, we also identified key ancestry-predictive eQTL and established classification models with promising training and testing accuracies in separating samples from close populations.

CONCLUSIONS

In summary, we developed a gene-based PLS procedure and a SAS macro for identifying g-eQTL and s-eQTL. We established data archives of eQTL for global populations. The program and data archives are accessible at http://www.stat.sinica.edu.tw/hsinchou/genetics/eQTL/HapMapII.htm. Finally, the results from our investigations regarding the interrelationship between eQTL, ancestry information, and pharmacodynamics provide rich resources for future eQTL studies and practical applications in population genetics and medical genetics.

Enhancer RNAs participate in androgen receptor-driven looping that selectively enhances gene activation

The androgen receptor (AR) is a key factor that regulates the behavior and fate of prostate cancer cells. The AR-regulated network is activated when AR binds enhancer elements and modulates specific enhancer-promoter looping. Kallikrein-related peptidase 3 (KLK3), which codes for prostate-specific antigen (PSA), is a well-known AR-regulated gene and its upstream enhancers produce bidirectional enhancer RNAs (eRNAs), termed KLK3e. Here, we demonstrate that KLK3e facilitates the spatial interaction of the KLK3 enhancer and the KLK2 promoter and enhances long-distance KLK2 transcriptional activation. KLK3e carries the core enhancer element derived from the androgen response element III (ARE III), which is required for the interaction of AR and Mediator 1 (Med1). Furthermore, we show that KLK3e processes RNA-dependent enhancer activity depending on the integrity of core enhancer elements. The transcription of KLK3e was detectable and its expression is significantly correlated with KLK3 (R(2) = 0.6213, P < 5 × 10(-11)) and KLK2 (R(2) = 0.5893, P < 5 × 10(-10)) in human prostate tissues. Interestingly, RNAi silencing of KLK3e resulted in a modest negative effect on prostate cancer cell proliferation. Accordingly, we report that an androgen-induced eRNA scaffolds the AR-associated protein complex that modulates chromosomal architecture and selectively enhances AR-dependent gene expression.

Laying a solid foundation for Manhattan--'setting the functional basis for the post-GWAS era'

Genome-wide association studies (GWAS) have identified more than 8900 genetic variants, mainly single-nucleotide polymorphisms (SNPs), associated with hundreds of human traits and diseases, which define risk-associated loci. Variants that map to coding regions can affect protein sequence, translation rate, and alternative splicing, all of which influence protein function. However, the vast majority of sequence variants map to non-coding intergenic and intronic regions, and it has been much more challenging to assess the functional nature of these variants. Recent work annotating the non-coding regions of the genome has contributed to post-GWAS studies by facilitating the identification of the functional targets of risk-associated loci. Many non-coding genetic variants within risk-associated loci alter gene expression by modulating the activity of cis-regulatory elements. We review here these recent findings, discuss their implication for the post-GWAS era, and relate their importance to the interpretation of disease-associated mutations identified through whole-genome sequencing.

Gene-centric meta-analysis in 87,736 individuals of European ancestry identifies multiple blood-pressure-related loci

Blood pressure (BP) is a heritable risk factor for cardiovascular disease. To investigate genetic associations with systolic BP (SBP), diastolic BP (DBP), mean arterial pressure (MAP), and pulse pressure (PP), we genotyped ~50,000 SNPs in up to 87,736 individuals of European ancestry and combined these in a meta-analysis. We replicated findings in an independent set of 68,368 individuals of European ancestry. Our analyses identified 11 previously undescribed associations in independent loci containing 31 genes including PDE1A, HLA-DQB1, CDK6, PRKAG2, VCL, H19, NUCB2, RELA, HOXC@ complex, FBN1, and NFAT5 at the Bonferroni-corrected array-wide significance threshold (p < 6 × 10(-7)) and confirmed 27 previously reported associations. Bioinformatic analysis of the 11 loci provided support for a putative role in hypertension of several genes, such as CDK6 and NUCB2. Analysis of potential pharmacological targets in databases of small molecules showed that ten of the genes are predicted to be a target for small molecules. In summary, we identified previously unknown loci associated with BP. Our findings extend our understanding of genes involved in BP regulation, which may provide new targets for therapeutic intervention or drug response stratification.

An expressed retrogene of the master embryonic stem cell gene POU5F1 is associated with prostate cancer susceptibility

Genetic association studies of prostate and other cancers have identified a major risk locus at chromosome 8q24. Several independent risk variants at this locus alter transcriptional regulatory elements, but an affected gene and mechanism for cancer predisposition have remained elusive. The retrogene POU5F1B within the locus has a preserved open reading frame encoding a homolog of the master embryonic stem cell transcription factor Oct4. We find that 8q24 risk alleles are expression quantitative trait loci correlated with reduced expression of POU5F1B in prostate tissue and that predicted deleterious POU5F1B missense variants are also associated with risk of transformation. POU5F1 is known to be self-regulated by the encoded Oct4 transcription factor. We further observe that POU5F1 expression is directly correlated with POU5F1B expression. Our results suggest that a pathway critical to self-renewal of embryonic stem cells may also have a role in the origin of cancer.

Novel insights into breast cancer genetic variance through RNA sequencing

Using RNA sequencing of triple-negative breast cancer (TNBC), non-TBNC and HER2-positive breast cancer sub-types, here we report novel expressed variants, allelic prevalence and abundance, and coexpression with other variation, and splicing signatures. To reveal the most prevalent variant alleles, we overlaid our findings with cancer- and population-based datasets and validated a subset of novel variants of cancer-related genes: ESRP2, GBP1, TPP1, MAD2L1BP, GLUD2 and SLC30A8. As a proof-of-principle, we demonstrated that a rare substitution in the splicing coordinator ESRP2 (R353Q) impairs its ability to bind to its substrate FGFR2 pre-mRNA. In addition, we describe novel SNPs and INDELs in cancer relevant genes with no prior reported association of point mutations with cancer, such as MTAP and MAGED1. For the first time, this study illustrates the power of RNA-sequencing in revealing the variation landscape of breast transcriptome and exemplifies analytical strategies to search regulatory interactions among cancer relevant molecules.

The relationship between DNA methylation, genetic and expression inter-individual variation in untransformed human fibroblasts

BACKGROUND

DNA methylation plays an essential role in the regulation of gene expression. While its presence near the transcription start site of a gene has been associated with reduced expression, the variation in methylation levels across individuals, its environmental or genetic causes, and its association with gene expression remain poorly understood.

RESULTS

We report the joint analysis of sequence variants, gene expression and DNA methylation in primary fibroblast samples derived from a set of 62 unrelated individuals. Approximately 2% of the most variable CpG sites are mappable in cis to sequence variation, usually within 5 kb. Via eQTL analysis with microarray data combined with mapping of allelic expression regions, we obtained a set of 2,770 regions mappable in cis to sequence variation. In 9.5% of these expressed regions, an associated SNP was also a methylation QTL. Methylation and gene expression are often correlated without direct discernible involvement of sequence variation, but not always in the expected direction of negative for promoter CpGs and positive for gene-body CpGs. Population-level correlation between methylation and expression is strongest in a subset of developmentally significant genes, including all four HOX clusters. The presence and sign of this correlation are best predicted using specific chromatin marks rather than position of the CpG site with respect to the gene.

CONCLUSIONS

Our results indicate a wide variety of relationships between gene expression, DNA methylation and sequence variation in untransformed adult human fibroblasts, with considerable involvement of chromatin features and some discernible involvement of sequence variation.

Genome-wide association studies and the clinic: a focus on breast cancer

Breast cancer is the most frequently diagnosed cancer among women worldwide, and has long been considered to be a genetic disease. A wide range of genetic variants, both rare mutations and more common variants, have been shown to influence breast cancer risk. In particular, recent studies have identified a number of common genetic variants, or single nucleotide polymorphisms, that are associated with breast cancer risk. In this review, we will briefly present the genetic epidemiology of breast cancer, genome-wide association study technology and how this technology may influence breast cancer screening in the clinic.

Fine-scale mapping of the FGFR2 breast cancer risk locus: putative functional variants differentially bind FOXA1 and E2F1

The 10q26 locus in the second intron of FGFR2 is the locus most strongly associated with estrogen-receptor-positive breast cancer in genome-wide association studies. We conducted fine-scale mapping in case-control studies genotyped with a custom chip (iCOGS), comprising 41 studies (n = 89,050) of European ancestry, 9 Asian ancestry studies (n = 13,983), and 2 African ancestry studies (n = 2,028) from the Breast Cancer Association Consortium. We identified three statistically independent risk signals within the locus. Within risk signals 1 and 3, genetic analysis identified five and two variants, respectively, highly correlated with the most strongly associated SNPs. By using a combination of genetic fine mapping, data on DNase hypersensitivity, and electrophoretic mobility shift assays to study protein-DNA binding, we identified rs35054928, rs2981578, and rs45631563 as putative functional SNPs. Chromatin immunoprecipitation showed that FOXA1 preferentially bound to the risk-associated allele (C) of rs2981578 and was able to recruit ERα to this site in an allele-specific manner, whereas E2F1 preferentially bound the risk variant of rs35054928. The risk alleles were preferentially found in open chromatin and bound by Ser5 phosphorylated RNA polymerase II, suggesting that the risk alleles are associated with changes in transcription. Chromatin conformation capture demonstrated that the risk region was able to interact with the promoter of FGFR2, the likely target gene of this risk region. A role for FOXA1 in mediating breast cancer susceptibility at this locus is consistent with the finding that the FGFR2 risk locus primarily predisposes to estrogen-receptor-positive disease.

The 5p12 breast cancer susceptibility locus affects MRPS30 expression in estrogen-receptor positive tumors

Genome-wide association studies have identified numerous loci linked to breast cancer susceptibility, but the mechanism by which variations at these loci influence susceptibility is usually unknown. Some variants are only associated with particular clinical subtypes of breast cancer. Understanding how and why these variants influence subtype-specific cancer risk contributes to our understanding of cancer etiology. We conducted a genome-wide expression Quantitative Trait Locus (eQTL) study in a discovery set of 287 breast tumors and 97 normal mammary tissue samples and a replication set of 235 breast tumors. We found that the risk-associated allele of rs7716600 in the 5p12 estrogen receptor-positive (ER-positive) susceptibility locus was associated with elevated expression of the nearby gene MRPS30 exclusively in ER-positive tumors. We replicated this finding in 235 independent tumors. Further, we showed the rs7716600 risk genotype was associated with decreased MRPS30 promoter methylation exclusively in ER-positive breast tumors. In vitro studies in MCF-7 cells carrying the protective genotype showed that estrogen stimulation decreased MRPS30 promoter chromatin availability and mRNA levels. In contrast, in 600MPE cells carrying the risk genotype, estrogen increased MRPS30 expression and did not affect promoter availability. Our data suggest the 5p12 risk allele affects MRPS30 expression in estrogen-responsive tumor cells after tumor initiation by a mechanism affecting chromatin availability. These studies emphasize that the genetic architecture of breast cancer is context-specific, and integrated analysis of gene expression and chromatin remodeling in normal and tumor tissues will be required to explain the mechanisms of risk alleles.

Unmasking risk loci: DNA methylation illuminates the biology of cancer predisposition: analyzing DNA methylation of transcriptional enhancers reveals missed regulatory links between cancer risk loci and genes

Paradoxically, DNA sequence polymorphisms in cancer risk loci rarely correlate with the expression of cancer genes. Therefore, the molecular mechanism underlying an individual's susceptibility to cancer has remained largely unknown. However, recent evaluations of the correlations between DNA methylation and gene expression levels across healthy and cancerous genomes have revealed enrichment of disease-related DNA methylation variations within disease-associated risk loci. Moreover, it appears that transcriptional enhancers embedded in cancer risk loci often contain DNA methylation sites that closely define the expression of prominent cancer genes, despite the lack of significant correlations between gene expression levels and the surrounding disease-associated polymorphic sequences. We suggest that DNA methylation variations may obscure the effect of co-residing risk sequence alleles. Analysis of enhancer methylation data may help to reveal the regulatory circuits underlying predisposition to cancers and other common diseases.

Beyond GWASs: illuminating the dark road from association to function

Genome-wide association studies (GWASs) have enabled the discovery of common genetic variation contributing to normal and pathological traits and clinical drug responses, but recognizing the precise targets of these associations is now the major challenge. Here, we review recent approaches to the functional follow-up of GWAS loci, including fine mapping of GWAS signal(s), prioritization of putative functional SNPs by the integration of genetic epidemiological and bioinformatic methods, and in vitro and in vivo experimental verification of predicted molecular mechanisms for identifying the targeted genes. The majority of GWAS-identified variants fall in noncoding regions of the genome. Therefore, this review focuses on strategies for assessing likely mechanisms affected by noncoding variants; such mechanisms include transcriptional regulation, noncoding RNA function, and epigenetic regulation. These approaches have already accelerated progress from genetic studies to biological knowledge and might ultimately guide the development of prognostic, preventive, and therapeutic measures.

Lessons from postgenome-wide association studies: functional analysis of cancer predisposition loci

In the last few years, genome-wide association studies (GWASs) have identified hundreds of predisposition loci for several types of human cancers. Recent progress has been made in determining the underlying mechanisms through which different single-nucleotide polymorphisms (SNPs) affect predisposition to cancer. Although there has been much debate about the clinical utility of GWASs, less attention has been paid to how GWASs and post-GWASs functional analysis have contributed to understanding the aetiology of cancer. Most common variants associated with cancer risk are localized in nonprotein-coding regions highlighting transcriptional regulation as a common theme in the mechanism of cancer predisposition. Here, we outline strategies to functionally dissect predisposition loci and discuss their limitations as well as challenges for future studies.

Hereditary breast cancer: ever more pieces to the polygenic puzzle

Several susceptibility genes differentially impact on the lifetime risk for breast cancer. Technological advances over the past years have enabled the detection of genetic risk factors through high-throughput screening of large breast cancer case-control series. High- to intermediate penetrance alleles have now been identified in more than 20 genes involved in DNA damage signalling and repair, and more than 70 low-penetrance loci have been discovered through recent genome-wide association studies. In addition to classical germ-line mutation and single-nucleotide polymorphism, copy number variation and somatic mosaicism have been proposed as potential predisposing mechanisms. Many of the identified loci also appear to influence breast tumour characteristics such as estrogen receptor status. In this review, we briefly summarize present knowledge about breast cancer susceptibility genes and discuss their implications for risk prediction and clinical practice.

Inherited susceptibility to chronic lymphocytic leukemia: evidence and prospects for the future

Chronic lymphocytic leukemia (CLL) is the most common leukemia in the United States and one of the most heritable cancers. A family history of the disease is perhaps the best defined risk factor, and approximately 15-20% of CLL patients have a family member with CLL or a related lymphoproliferative disorder. Much effort has been devoted to trying to elucidate the mechanisms underlying this genetic risk. Familial CLL appears to be clinically and biologically similar to sporadic CLL, and most if not all CLL appears to be preceded by monoclonal B-cell lymphocytosis (MBL), which does appear to occur at higher frequency in relatives in families with CLL. Neither linkage studies nor candidate gene association studies have proven particularly informative in CLL, but genomewide association studies have identified multiple low-risk variants that together explain about 16% of the familial risk of CLL. Studies of individual families have identified higher-risk single nucleotide polymorphisms or copy number variants associated with disease risk in those families. Current efforts to identify additional risk loci are focused on applying next-generation sequencing to the germline of informative CLL families as well as individuals with sporadic CLL.

Lessons from functional analysis of genome-wide association studies

Most cancer-associated single-nucleotide polymorphisms (SNP) identified using genome-wide association studies are located outside of protein-coding regions, and their significance and mode of action have been a source of continuing debate. One proposed mechanism of action of the SNPs is that they would affect the activity of enhancer elements regulating critical target genes. In this review, we summarize recent results that substantiate this model. These studies have identified a cancer-specific enhancer element at the 8q24 gene desert that controls the expression of the MYC oncogene. We further discuss implications of the observed difference between normal growth control and cancer for drug development, and the inherent features of genome-wide association studies that may specifically lead to identification of disease-specific regulatory elements.

Comprehensive functional annotation of seventy-one breast cancer risk Loci

Breast Cancer (BCa) genome-wide association studies revealed allelic frequency differences between cases and controls at index single nucleotide polymorphisms (SNPs). To date, 71 loci have thus been identified and replicated. More than 320,000 SNPs at these loci define BCa risk due to linkage disequilibrium (LD). We propose that BCa risk resides in a subgroup of SNPs that functionally affects breast biology. Such a shortlist will aid in framing hypotheses to prioritize a manageable number of likely disease-causing SNPs. We extracted all the SNPs, residing in 1 Mb windows around breast cancer risk index SNP from the 1000 genomes project to find correlated SNPs. We used FunciSNP, an R/Bioconductor package developed in-house, to identify potentially functional SNPs at 71 risk loci by coinciding them with chromatin biofeatures. We identified 1,005 SNPs in LD with the index SNPs (r(2)≥0.5) in three categories; 21 in exons of 18 genes, 76 in transcription start site (TSS) regions of 25 genes, and 921 in enhancers. Thirteen SNPs were found in more than one category. We found two correlated and predicted non-benign coding variants (rs8100241 in exon 2 and rs8108174 in exon 3) of the gene, ANKLE1. Most putative functional LD SNPs, however, were found in either epigenetically defined enhancers or in gene TSS regions. Fifty-five percent of these non-coding SNPs are likely functional, since they affect response element (RE) sequences of transcription factors. Functionality of these SNPs was assessed by expression quantitative trait loci (eQTL) analysis and allele-specific enhancer assays. Unbiased analyses of SNPs at BCa risk loci revealed new and overlooked mechanisms that may affect risk of the disease, thereby providing a valuable resource for follow-up studies.

Deconvoluting complex tissues for expression quantitative trait locus-based analyses

Breast cancer genome-wide association studies have pinpointed dozens of variants associated with breast cancer pathogenesis. The majority of risk variants, however, are located outside of known protein-coding regions. Therefore, identifying which genes the risk variants are acting through presents an important challenge. Variants that are associated with mRNA transcript levels are referred to as expression quantitative trait loci (eQTLs). Many studies have demonstrated that eQTL-based strategies provide a direct way to connect a trait-associated locus with its candidate target gene. Performing eQTL-based analyses in human samples is complicated because of the heterogeneous nature of human tissue. We addressed this issue by devising a method to computationally infer the fraction of cell types in normal human breast tissues. We then applied this method to 13 known breast cancer risk loci, which we hypothesized were eQTLs. For each risk locus, we took all known transcripts within a 2 Mb interval and performed an eQTL analysis in 100 reduction mammoplasty cases. A total of 18 significant associations were discovered (eight in the epithelial compartment and 10 in the stromal compartment). This study highlights the ability to perform large-scale eQTL studies in heterogeneous tissues.

GWAS meets TCGA to illuminate mechanisms of cancer predisposition

Genome-wide association studies (GWASs) have unraveled a large number of cancer risk alleles. Understanding how these allelic variants predispose to disease is a major bottleneck confronting translational application. In this issue, Li and colleagues combine GWASs with The Cancer Genome Atlas (TCGA) to disambiguate the contributions of germline and somatic variants to tumorigenic gene expression programs. They find that close to half of the known risk alleles for estrogen receptor (ER)-positive breast cancer are expression quantitative trait loci (eQTLs) acting upon major determinants of gene expression in tumors.