Hierarchical modeling identifies novel lung cancer susceptibility variants in inflammation pathways among 10,140 cases and 11,012 controls

Recent evidence suggests that inflammation plays a pivotal role in the development of lung cancer. In this study, we used a two-stage approach to investigate associations between genetic variants in inflammation pathways and lung cancer risk based on genome-wide association study (GWAS) data. A total of 7,650 sequence variants from 720 genes relevant to inflammation pathways were identified using keyword and pathway searches from Gene Cards and Gene Ontology databases. In Stage 1, six GWAS datasets from the International Lung Cancer Consortium were pooled (4,441 cases and 5,094 controls of European ancestry), and a hierarchical modeling (HM) approach was used to incorporate prior information for each of the variants into the analysis. The prior matrix was constructed using (1) role of genes in the inflammation and immune pathways; (2) physical properties of the variants including the location of the variants, their conservation scores and amino acid coding; (3) LD with other functional variants and (4) measures of heterogeneity across the studies. HM affected the priority ranking of variants particularly among those having low prior weights, imprecise estimates and/or heterogeneity across studies. In Stage 2, we used an independent NCI lung cancer GWAS study (5,699 cases and 5,818 controls) for in silico replication. We identified one novel variant at the level corrected for multiple comparisons (rs2741354 in EPHX2 at 8q21.1 with p value = 7.4 × 10^?6), and confirmed the associations between TERT (rs2736100) and the HLA region and lung cancer risk. HM allows for prior knowledge such as from bioinformatic sources to be incorporated into the analysis systematically, and it represents a complementary analytical approach to the conventional GWAS analysis.     

Characterizing genetic risk at known prostate cancer susceptibility loci in African Americans

GWAS of prostate cancer have been remarkably successful in revealing common genetic variants and novel biological pathways that are linked with its etiology. A more complete understanding of inherited susceptibility to prostate cancer in the general population will come from continuing such discovery efforts and from testing known risk alleles in diverse racial and ethnic groups. In this large study of prostate cancer in African American men (3,425 prostate cancer cases and 3,290 controls), we tested 49 risk variants located in 28 genomic regions identified through GWAS in men of European and Asian descent, and we replicated associations (at p≤0.05) with roughly half of these markers. Through fine-mapping, we identified nearby markers in many regions that better define associations in African Americans. At 8q24, we found 9 variants (p≤6×10⁻⁴) that best capture risk of prostate cancer in African Americans, many of which are more common in men of African than European descent. The markers found to be associated with risk at each locus improved risk modeling in African Americans (per allele OR = 1.17) over the alleles reported in the original GWAS (OR = 1.08). In summary, in this detailed analysis of the prostate cancer risk loci reported from GWAS, we have validated and improved upon markers of risk in some regions that better define the association with prostate cancer in African Americans. Our findings with variants at 8q24 also reinforce the importance of this region as a major risk locus for prostate cancer in men of African ancestry.     

Multiple Functional Risk Variants in a SMAD7 Enhancer Implicate a Colorectal Cancer Risk Haplotype

Genome-wide association studies (GWAS) of colorectal cancer (CRC) have led to the identification of a number of common variants associated with modest risk. Several risk variants map within the vicinity of TGFβ/BMP signaling pathway genes, including rs4939827 within an intron of SMAD7 at 18q21.1. A previous study implicated a novel SNP (novel 1 or rs58920878) as a functional variant within an enhancer element in SMAD7 intron 4. In this study, we show that four SNPs including novel 1 (rs6507874, rs6507875, rs8085824, and rs58920878) in linkage disequilibrium (LD) with the index SNP rs4939827 demonstrate allele-specific enhancer effects in a large, multi-component enhancer of SMAD7. All four SNPs demonstrate allele-specific protein binding to nuclear extracts of CRC cell lines. Furthermore, some of the risk-associated alleles correlate with increased expression of SMAD7 in normal colon tissues. Finally, we show that the enhancer is responsive to BMP4 stimulation. Taken together, we propose that the associated CRC risk at 18q21.1 is due to four functional variants that regulate SMAD7 expression and potentially perturb a BMP negative feedback loop in TGFβ/BMP signaling pathways.     

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.     

Quantitative statistical analysis of cis-regulatory sequences in ABA/VP1- and CBF/DREB1-regulated genes of Arabidopsis

We have developed a simple quantitative computational approach for objective analysis of cis-regulatory sequences in promoters of coregulated genes. The program, designated MotifFinder, identifies oligo sequences that are overrepresented in promoters of coregulated genes. We used this approach to analyze promoter sequences of Viviparous1 (VP1)/abscisic acid (ABA)-regulated genes and cold-regulated genes, respectively, of Arabidopsis (Arabidopsis thaliana). We detected significantly enriched sequences in up-regulated genes but not in down-regulated genes. This result suggests that gene activation but not repression is mediated by specific and common sequence elements in promoters. The enriched motifs include several known cis-regulatory sequences as well as previously unidentified motifs. With respect to known cis-elements, we dissected the flanking nucleotides of the core sequences of Sph element, ABA response elements (ABREs), and the C repeat/dehydration-responsive element. This analysis identified the motif variants that may correlate with qualitative and quantitative differences in gene expression. While both VP1 and cold responses are mediated in part by ABA signaling via ABREs, these responses correlate with unique ABRE variants distinguished by nucleotides flanking the ACGT core. ABRE and Sph motifs are tightly associated uniquely in the coregulated set of genes showing a strict dependence on VP1 and ABA signaling. Finally, analysis of distribution of the enriched sequences revealed a striking concentration of enriched motifs in a proximal 200-base region of VP1/ABA and cold-regulated promoters. Overall, each class of coregulated genes possesses a discrete set of the enriched motifs with unique distributions in their promoters that may account for the specificity of gene regulation.     

Insights into Colon Cancer Etiology via a Regularized Approach to Gene Set Analysis of GWAS Data

Genome-wide association studies (GWAS) have successfully identified susceptibility loci from marginal association analysis of SNPs. Valuable insight into genetic variation underlying complex diseases will likely be gained by considering functionally related sets of genes simultaneously. One approach is to further develop gene set enrichment analysis methods, which are initiated in gene expression studies, to account for the distinctive features of GWAS data. These features include the large number of SNPs per gene, the modest and sparse SNP associations, and the additional information provided by linkage disequilibrium (LD) patterns within genes. We propose a “gene set ridge regression in association studies (GRASS)” algorithm. GRASS summarizes the genetic structure for each gene as eigenSNPs and uses a novel form of regularized regression technique, termed group ridge regression, to select representative eigenSNPs for each gene and assess their joint association with disease risk. Compared with existing methods, the proposed algorithm greatly reduces the high dimensionality of GWAS data while still accounting for multiple hits and/or LD in the same gene. We show by simulation that this algorithm performs well in situations in which there are a large number of predictors compared to sample size. We applied the GRASS algorithm to a genome-wide association study of colon cancer and identified nicotinate and nicotinamide metabolism and transforming growth factor beta signaling as the top two significantly enriched pathways. Elucidating the role of variation in these pathways may enhance our understanding of colon cancer etiology.     

Body Mass Index Genetic Risk Score and Endometrial Cancer Risk

Genome-wide association studies (GWAS) have identified common variants that predispose individuals to a higher body mass index (BMI), an independent risk factor for endometrial cancer. Composite genotype risk scores (GRS) based on the joint effect of published BMI risk loci were used to explore whether endometrial cancer shares a genetic background with obesity. Genotype and risk factor data were available on 3,376 endometrial cancer case and 3,867 control participants of European ancestry from the Epidemiology of Endometrial Cancer Consortium GWAS. A BMI GRS was calculated by summing the number of BMI risk alleles at 97 independent loci. For exploratory analyses, additional GRSs were based on subsets of risk loci within putative etiologic BMI pathways. The BMI GRS was statistically significantly associated with endometrial cancer risk (P = 0.002). For every 10 BMI risk alleles a woman had a 13% increased endometrial cancer risk (95% CI: 4%, 22%). However, after adjusting for BMI, the BMI GRS was no longer associated with risk (per 10 BMI risk alleles OR = 0.99, 95% CI: 0.91, 1.07; P = 0.78). Heterogeneity by BMI did not reach statistical significance (P = 0.06), and no effect modification was noted by age, GWAS Stage, study design or between studies (P≥0.58). In exploratory analyses, the GRS defined by variants at loci containing monogenic obesity syndrome genes was associated with reduced endometrial cancer risk independent of BMI (per BMI risk allele OR = 0.92, 95% CI: 0.88, 0.96; P = 2.1 x 10⁻⁵). Possessing a large number of BMI risk alleles does not increase endometrial cancer risk above that conferred by excess body weight among women of European descent. Thus, the GRS based on all current established BMI loci does not provide added value independent of BMI. Future studies are required to validate the unexpected observed relation between monogenic obesity syndrome genetic variants and endometrial cancer risk.     

Multivariate eQTL mapping uncovers functional variation on the X-chromosome associated with complex disease traits

Very few studies have investigated the associations between genetic polymorphisms and gene expression on the X-chromosome. This is a major bottleneck when conducting functional follow-up studies of trait-associated variants, as those identified in genome-wide association studies (GWAS). We used a multivariate approach to test the association between individual single nucleotide polymorphisms (SNPs) and exon expression levels measured in 356 Epstein–Barr virus-transformed lymphoblastoid cell lines (LCLs) from the Geuvadis RNA sequencing project to identify SNPs associated with variation in gene expression on the X-chromosome, which we refer to as eSNPs. At an FDR of 5 %, we discovered 548 independent [linkage disequilibrium (LD) r ² < 0.1] eSNPs on the X-chromosome. Of these, 35 were in LD (r ² > 0.8) with previously published disease- or trait-associated variants identified through GWAS. One of the strongest eSNPs identified was rs35975601, which was associated with F8A1 expression (p value = 3 × 10^?20) and was in LD with a type 1 diabetes risk variant. Additionally, we identified a number of genes for which eSNPs were in LD with multiple diseases or traits, including DNASE1L1 which was mapped to bilirubin levels, type 1 diabetes and schizophrenia. Our results also indicate that multivariate exon-level analysis provides a more powerful approach than univariate gene-level analysis, particularly when SNPs influence the expression of different exons with different magnitude and/or direction of effect. The associations identified in our study may provide new insights into the molecular process by which gene expression may contribute to trait variation or disease risk in humans.     

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.     

Genome-Wide Testing of Putative Functional Exonic Variants in Relationship with Breast and Prostate Cancer Risk in a Multiethnic Population

Rare variation in protein coding sequence is poorly captured by GWAS arrays and has been hypothesized to contribute to disease heritability. Using the Illumina HumanExome SNP array, we successfully genotyped 191,032 common and rare non-synonymous, splice site, or nonsense variants in a multiethnic sample of 2,984 breast cancer cases, 4,376 prostate cancer cases, and 7,545 controls. In breast cancer, the strongest associations included either SNPs in or gene burden scores for genes LDLRAD1, SLC19A1, FGFBP3, CASP5, MMAB, SLC16A6, and INS-IGF2. In prostate cancer, one of the most associated SNPs was in the gene GPRC6A (rs2274911, Pro91Ser, OR = 0.88, P = 1.3×10⁻⁵) near to a known risk locus for prostate cancer; other suggestive associations were noted in genes such as F13A1, ANXA4, MANSC1, and GP6. For both breast and prostate cancer, several of the most significant associations involving SNPs or gene burden scores (sum of minor alleles) were noted in genes previously reported to be associated with a cancer-related phenotype. However, only one of the associations (rs145889899 in LDLRAD1, p = 2.5×10⁻⁷ only seen in African Americans) for overall breast or prostate cancer risk was statistically significant after correcting for multiple comparisons. In addition to breast and prostate cancer, other cancer-related traits were examined (body mass index, PSA level, and alcohol drinking) with a number of known and potentially novel associations described. In general, these findings do not support there being many protein coding variants of moderate to high risk for breast and prostate cancer with odds ratios over a range that is probably required for protein coding variation to play a truly outstanding role in risk heritability. Very large sample sizes will be required to better define the role of rare and less penetrant coding variation in prostate and breast cancer disease genetics.     

Association analysis of 9,560 prostate cancer cases from the International Consortium of Prostate Cancer Genetics confirms the role of reported prostate cancer associated SNPs for familial disease

Previous GWAS studies have reported significant associations between various common SNPs and prostate cancer risk using cases unselected for family history. How these variants influence risk in familial prostate cancer is not well studied. Here, we analyzed 25 previously reported SNPs across 14 loci from prior prostate cancer GWAS. The International Consortium for Prostate Cancer Genetics (ICPCG) previously validated some of these using a family-based association method (FBAT). However, this approach suffered reduced power due to the conditional statistics implemented in FBAT. Here, we use a case–control design with an empirical analysis strategy to analyze the ICPCG resource for association between these 25 SNPs and familial prostate cancer risk. Fourteen sites contributed 12,506 samples (9,560 prostate cancer cases, 3,368 with aggressive disease, and 2,946 controls from 2,283 pedigrees). We performed association analysis with Genie software which accounts for relationships. We analyzed all familial prostate cancer cases and the subset of aggressive cases. For the familial prostate cancer phenotype, 20 of the 25 SNPs were at least nominally associated with prostate cancer and 16 remained significant after multiple testing correction (p ≤ 1E ^?3) occurring on chromosomal bands 6q25, 7p15, 8q24, 10q11, 11q13, 17q12, 17q24, and Xp11. For aggressive disease, 16 of the SNPs had at least nominal evidence and 8 were statistically significant including 2p15. The results indicate that the majority of common, low-risk alleles identified in GWAS studies for all prostate cancer also contribute risk for familial prostate cancer, and that some may contribute risk to aggressive disease.     

Fine-Mapping the HOXB Region Detects Common Variants Tagging a Rare Coding Allele: Evidence for Synthetic Association in Prostate Cancer

The HOXB13 gene has been implicated in prostate cancer (PrCa) susceptibility. We performed a high resolution fine-mapping analysis to comprehensively evaluate the association between common genetic variation across the HOXB genetic locus at 17q21 and PrCa risk. This involved genotyping 700 SNPs using a custom Illumina iSelect array (iCOGS) followed by imputation of 3195 SNPs in 20,440 PrCa cases and 21,469 controls in The PRACTICAL consortium. We identified a cluster of highly correlated common variants situated within or closely upstream of HOXB13 that were significantly associated with PrCa risk, described by rs117576373 (OR 1.30, P = 2.62×10⁻¹⁴). Additional genotyping, conditional regression and haplotype analyses indicated that the newly identified common variants tag a rare, partially correlated coding variant in the HOXB13 gene (G84E, rs138213197), which has been identified recently as a moderate penetrance PrCa susceptibility allele. The potential for GWAS associations detected through common SNPs to be driven by rare causal variants with higher relative risks has long been proposed; however, to our knowledge this is the first experimental evidence for this phenomenon of synthetic association contributing to cancer susceptibility.     

Analysis of the 10q11 cancer risk locus implicates MSMB and NCOA4 in human prostate tumorigenesis

Genome-wide association studies (GWAS) have established a variant, rs10993994, on chromosome 10q11 as being associated with prostate cancer risk. Since the variant is located outside of a protein-coding region, the target genes driving tumorigenesis are not readily apparent. Two genes nearest to this variant, MSMB and NCOA4, are strong candidates for mediating the effects of rs109939934. In a cohort of 180 individuals, we demonstrate that the rs10993994 risk allele is associated with decreased expression of two MSMB isoforms in histologically normal and malignant prostate tissue. In addition, the risk allele is associated with increased expression of five NCOA4 isoforms in histologically normal prostate tissue only. No consistent association with either gene is observed in breast or colon tissue. In conjunction with these findings, suppression of MSMB expression or NCOA4 overexpression promotes anchorage-independent growth of prostate epithelial cells, but not growth of breast epithelial cells. These data suggest that germline variation at chromosome 10q11 contributes to prostate cancer risk by influencing expression of at least two genes. More broadly, the findings demonstrate that disease risk alleles may influence multiple genes, and associations between genotype and expression may only be observed in the context of specific tissue and disease states.     

A genome-wide association study of upper aerodigestive tract cancers conducted within the INHANCE consortium

Genome-wide association studies (GWAS) have been successful in identifying common genetic variation involved in susceptibility to etiologically complex disease. We conducted a GWAS to identify common genetic variation involved in susceptibility to upper aero-digestive tract (UADT) cancers. Genome-wide genotyping was carried out using the Illumina HumanHap300 beadchips in 2,091 UADT cancer cases and 3,513 controls from two large European multi-centre UADT cancer studies, as well as 4,821 generic controls. The 19 top-ranked variants were investigated further in an additional 6,514 UADT cancer cases and 7,892 controls of European descent from an additional 13 UADT cancer studies participating in the INHANCE consortium. Five common variants presented evidence for significant association in the combined analysis (p≤5×10⁻⁷). Two novel variants were identified, a 4q21 variant (rs1494961, p = 1×10⁻⁸) located near DNA repair related genes HEL308 and FAM175A (or Abraxas) and a 12q24 variant (rs4767364, p = 2×10⁻⁸) located in an extended linkage disequilibrium region that contains multiple genes including the aldehyde dehydrogenase 2 (ALDH2) gene. Three remaining variants are located in the ADH gene cluster and were identified previously in a candidate gene study involving some of these samples. The association between these three variants and UADT cancers was independently replicated in 5,092 UADT cancer cases and 6,794 controls non-overlapping samples presented here (rs1573496-ADH7, p = 5×10⁻⁸; rs1229984-ADH1B, p = 7×10⁻⁹; and rs698-ADH1C, p = 0.02). These results implicate two variants at 4q21 and 12q24 and further highlight three ADH variants in UADT cancer susceptibility.     

Etiologic impact of known cancer susceptibility genes

The impact of a gene variant on the population burden of cancer can be measured by the population attributable fraction (PAF), which depends on the risk conferred by the variant, genotype relative risk (GRR), the frequency of the variant in the population and the mode of inheritance. PAF defines the proportion of the disease in the study population due to a gene variant, hence the synonymic term, etiologic fraction. After a review of the literature, 27 confirmed cancer susceptibility genes, groups of genes and loci were selected for analysis on the basis of their prevalence and availability of validated GRR data. The covered variants represent the most common established cancer susceptibility genes; those not included have marginal PAFs on common cancers. The PAF due to known genes at the covered sites was highest for brain hemangioblastoma (19%), conferred by the VHL gene. For colorectal cancer, the PAF estimates amounted to 7.0%. Including genes and identified loci from whole genome scans, PAFs for both breast and prostate cancers summed up to 70%. The derived estimates should rectify common overstatements on the contribution of individual high penetrance genes on common cancers at the population level. More dramatically, the estimates show the large PAFs conferred by the recently discovered breast, prostate and colorectal cancer loci, most of which are not known to alter coding sequences or expression patterns and they thus act through yet unexplained mechanisms. Although of low risk, these common variants appear to explain large proportions of breast and prostate cancers in the population.     

cis-Expression QTL analysis of established colorectal cancer risk variants in colon tumors and adjacent normal tissue

Genome-wide association studies (GWAS) have identified 19 risk variants associated with colorectal cancer. As most of these risk variants reside outside the coding regions of genes, we conducted cis-expression quantitative trait loci (cis-eQTL) analyses to investigate possible regulatory functions on the expression of neighboring genes. Forty microsatellite stable and CpG island methylator phenotype-negative colorectal tumors and paired adjacent normal colon tissues were used for genome-wide SNP and gene expression profiling. We found that three risk variants (rs10795668, rs4444235 and rs9929218, using near perfect proxies rs706771, rs11623717 and rs2059252, respectively) were significantly associated (FDR q-value ≤0.05) with expression levels of nearby genes (<2 Mb up- or down-stream). We observed an association between the low colorectal cancer risk allele (A) for rs10795668 at 10p14 and increased expression of ATP5C1 (q = 0.024) and between the colorectal cancer high risk allele (C) for rs4444235 at 14q22.2 and increased expression of DLGAP5 (q = 0.041), both in tumor samples. The colorectal cancer low risk allele (A) for rs9929218 at 16q22.1 was associated with a significant decrease in expression of both NOL3 (q = 0.017) and DDX28 (q = 0.046) in the adjacent normal colon tissue samples. Of the four genes, DLGAP5 and NOL3 have been previously reported to play a role in colon carcinogenesis and ATP5C1 and DDX28 are mitochondrial proteins involved in cellular metabolism and division, respectively. The combination of GWAS findings, prior functional studies, and the cis-eQTL analyses described here suggest putative functional activities for three of the colorectal cancer GWAS identified risk loci as regulating the expression of neighboring genes.     

Alzheimer’s Disease Risk Polymorphisms Regulate Gene Expression in the ZCWPW1 and the CELF1 Loci

Late onset Alzheimer’s disease (LOAD) is a genetically complex and clinically heterogeneous disease. Recent large-scale genome wide association studies (GWAS) have identified more than twenty loci that modify risk for AD. Despite the identification of these loci, little progress has been made in identifying the functional variants that explain the association with AD risk. Thus, we sought to determine whether the novel LOAD GWAS single nucleotide polymorphisms (SNPs) alter expression of LOAD GWAS genes and whether expression of these genes is altered in AD brains. The majority of LOAD GWAS SNPs occur in gene dense regions under large linkage disequilibrium (LD) blocks, making it unclear which gene(s) are modified by the SNP. Thus, we tested for brain expression quantitative trait loci (eQTLs) between LOAD GWAS SNPs and SNPs in high LD with the LOAD GWAS SNPs in all of the genes within the GWAS loci. We found a significant eQTL between rs1476679 and PILRB and GATS, which occurs within the ZCWPW1 locus. PILRB and GATS expression levels, within the ZCWPW1 locus, were also associated with AD status. Rs7120548 was associated with MTCH2 expression, which occurs within the CELF1 locus. Additionally, expression of several genes within the CELF1 locus, including MTCH2, were highly correlated with one another and were associated with AD status. We further demonstrate that PILRB, as well as other genes within the GWAS loci, are most highly expressed in microglia. These findings together with the function of PILRB as a DAP12 receptor supports the critical role of microglia and neuroinflammation in AD risk.     

Sequence Analysis of Six Blood Pressure Candidate Regions in 4,178 Individuals: The Cohorts for Heart and Aging Research in Genomic Epidemiology (CHARGE) Targeted Sequencing Study

Background

Genome-wide association studies (GWAS) identified multiple loci for blood pressure (BP) and hypertension. Six genes – ATP2B1, CACNB2, CYP17A1, JAG1, PLEKHA7, and SH2B3 – were evaluated for sequence variation with large effects on systolic blood pressure (SBP), diastolic blood pressure (DBP), pulse pressure (PP), and mean arterial pressure (MAP).

Methods and Results

Targeted genomic sequence was determined in 4,178 European ancestry participants from the Cohorts for Heart and Aging Research in Genomic Epidemiology (CHARGE) Consortium. Common variants (≥50 minor allele copies) were evaluated individually and rare variants (minor allele frequency, MAF≤1%) were aggregated by locus. 464 common variants were identified across the 6 genes. An upstream CYP17A1 variant, rs11191416 (MAF = 0.09), was the most significant association for SBP (P = 0.0005); however the association was attenuated (P = 0.0469) after conditioning on the GWAS index single nucleotide polymorphism (SNP). A PLEKHA7 intronic variant was the strongest DBP association (rs12806040, MAF = 0.007, P = 0.0006) and was not in LD (r² = 0.01) with the GWAS SNP. A CACNB2 intronic SNP, rs1571787, was the most significant association with PP (MAF = 0.27, P = 0.0003), but was not independent from the GWAS SNP (r² = 0.34). Three variants (rs6163 and rs743572 in the CYP17A1 region and rs112467382 in PLEKHA7) were associated with BP traits (P<0.001). Rare variation, aggregately assessed in the 6 regions, was not significantly associated with BP measures.

Conclusion

Six targeted gene regions, previously identified by GWAS, did not harbor novel variation with large effects on BP in this sample.     

Identifying disease related sub-pathways for analysis of genome-wide association studies

Most methods for genome-wide association studies (GWAS) focus on discovering a single genetic variant, but the pathogenesis of complex diseases is thought to arise from the joint effect of multiple genetic variants. Information about pathway structure, such as the interactions and distances between gene products within pathways, can help us learn more about the functions and joint effect of genes associated with disease risk. We developed a novel sub-pathway based approach to study the joint effect of multiple genetic variants that are modestly associated with disease. The approach prioritized sub-pathways based on the significance values of single nucleotide polymorphisms (SNPs) and the interactions and distances between gene products within pathways. We applied the method to seven complex diseases. The result showed that our method can efficiently identify statistically significant sub-pathways associated with the pathogenesis of complex diseases. The approach identified sub-pathways that may inform the interpretation of GWAS data.