Jenkins, Gregory D.

Genome‐wide association studies (GWAS) of complex traits have generated many association signals for single nucleotide polymorphisms (SNPs). To understand the underlying causal genetic variant(s), focused DNA resequencing of targeted genomic regions is commonly used, yet the current cost of resequencing limits sample sizes for resequencing studies. Information from the large GWAS can be used to guide choice of samples for resequencing, such as the SNP genotypes in the targeted genomic region. Viewing the GWAS tag‐SNPs as imperfect surrogates for the underlying causal variants, yet expecting that the tag‐SNPs are correlated with the causal variants, a reasonable approach is a two‐phase case‐control design, with the GWAS serving as the first‐phase and the resequencing study serving as the second‐phase. Using stratified sampling based on both tag‐SNP genotypes and case‐control status, we explore the gains in power of a two‐phase design relative to randomly sampling cases and controls for resequencing (i.e., ignoring tag‐SNP genotypes). Simulation results show that stratified sampling based on both tag‐SNP genotypes and case‐control status is not likely to have lower power than stratified sampling based only on case‐control status, and can sometimes have substantially greater power. The gain in power depends on the amount of linkage disequilibrium between the tag‐SNP and causal variant alleles, as well as the effect size of the causal variant. Hence, the two‐phase design provides an efficient approach to follow‐up GWAS signals with DNA resequencing.

Abstract Epithelial ovarian cancer (EOC) is the 6th most commonly diagnosed cancer among women in the world, accounting for 4% of all female cancers. On a worldwide basis, an estimated 204,000 new cases are diagnosed and 125,000 women die of EOC annually, with 5-year survival rate at 45%. Metastasis and its complications are the usual cause of EOC-related deaths. Thus, factors related to tumor aggressiveness, response to therapy, and the underlying health state of the patient often predict survival. Candidate gene and genome-wide association studies (GWAS) for survival from EOC have discovered only a few the genetic variants, accounting for only a small proportion of the heritability. One explanation for the missing heritability is that the common analysis approach _ assessing the effect of each single nucleotide polymorphism (SNP) individually _ is not well suited to the detection of small effects of multiple SNPs. Gene set analysis (GSA) overcomes this limitation by assessing the overall evidence of association of a phenotype with all measured variation in a set of genes or pathway. Therefore, we conducted a GSA using data from an EOC GWAS, using the Illumina 610-quad SNP array, to determine novel gene sets (GSs) associated with overall survival (OS) from EOC (N cases = 1057, N deaths = 495). All subjects were non-Hispanic, non-Jewish Caucasians, with sample and SNP call rates >95%. Subjects with ambiguous gender, unresolved identical genotypes, and less than 80% European ancestry were also excluded. SNPs were mapped to genes within 20 kb, with genes then mapped to GSs defined by the following: KEGG, Gene Ontology (biological, cellular, molecular) and PharmGKB. GSA was completed in which the association of each gene with OS was assessed using a principal component gene-level test (adjusting for study site, age and population sub-structure based on the first eigenvector from EigenStrat), followed by the aggregation of the gene-level p-values using the Gamma method with a soft truncation threshold of 0.15. Empirical p-values were determined using permutation methods. Of the 2,573 GSs assessed, 30 GSs had a p-value < 0.01. The top GSs were related to: mismatch repair (p = 3.0E-4), platelet formation (p=0.001), leukocyte adhesive activation (p=0.002), antigen processing and presentation of endogenous antigens (p = 0.002) or peptide antigens (p = 0.002), platinum pathway (p = 0.003), aging and lifespan (p = 0.004, p = 0.007), immune system development (p = 0.004) and somatic diversification (p=0.007). These results illustrate the usefulness of GSA for the development of novel hypotheses that can be followed-up with additional clinical or functional studies. Moreover, application of GSA to survival from EOC allowed the detection of significant GSs (and the corresponding genes/SNPs within these GSs) that impact OS that were not detected by assessing each SNP individually, where the smallest p-value from single SNP analysis of the GWAS for OS was 1.3E-05. Citation Format: {Authors}. {Abstract title} [abstract]. In: Proceedings of the 102nd Annual Meeting of the American Association for Cancer Research; 2011 Apr 2-6; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2011;71(8 Suppl):Abstract nr 2738. doi:10.1158/1538-7445.AM2011-2738

Abstract Introduction: Androstenedione is a precursor for estrogen biosynthesis catalyzed by aromatase. Plasma androstenedione concentrations in postmenopausal women with early stage breast cancer display large interindividual variation (Ingle et al., Cancer Res. 70:3278–86, 2010). We performed a GWAS to identify single nucleotide polymorphisms (SNPs) associated with plasma levels of androstenedione in 776 postmenopausal women with resected early stage breast cancer. Methods: Plasma androstenedione concentrations were measured by GC-MS/MS. GWAS was performed using Illumina Human610-Quad BeadChips. The GWAS results were controlled for population stratification (PS) by Eigenstrat analyses. Plasma androstenedione concentrations were associated with genotype by using a quasi-likelihood model with adjustment for PS, BMI, age and other clinical variables associated with androstenedione concentrations (p<0.01). Imputation using HapMap2 data was performed within 200 kb on either side of regions containing SNPs with p<E-05. Results: 563,945 SNPs were analyzed, followed by imputation. The genotyped SNP with the lowest p value, rs4736317 (p=4.61E-06), was on chromosome 8 near the CYP11B1 and CYP11B2 genes. This SNP had a multiplicative effect of 1.128 (95% Cl: 1.07, 1.19) and a MAF of 0.40. After imputation, 20 SNPs with apparent p<E-06 were observed in this same region, with the lowest p value for rs9773022 (p=8.1E-08). We then performed functional genomic studies, and observed that two of the SNPs with low p values (rs4736312, p=1.49E-05 and rs4736350, p=6.34E-06) created apparent Oct-1 transcription factor binding sites (TFBS). Differential nuclear protein binding of Oct-1 to probes for these SNPs was verified with EMS and supershift assays using nuclear extract from A549 and NCI-H295R cells. Treatment of these two cell lines with 10 nM androstenedione led to the disruption of Oct-1 binding with the A (wild type) allele on rs4736312. rs4736312 is located in the 3'-UTR of CYP11B1 15 bp distant from response elements for the progesterone receptor and the glucocorticoid receptor. We also demonstrated that CYP11B1 and CYP11B2 were induced by estradiol in U2OS cell lines that express ER receptor and by androstenedione in H295R cells that express the androgen receptor. Transfection of a reporter gene construct with the rs4736312 SNP sequences into COS-1 and H295R cells suppressed reporter gene activity for the variant SNP sequence as compared to wild type, an observation that may correlate with the higher plasma levels of androstenedione in women (549× 328 vs 410×208 mean×SD, M/ml) with the variant allele. Conclusion: We have identified a series of SNPs in a region of chromosome 8 in and around CYP11B1 and CYP11B2 that are associated with elevated plasma androstenedione concentrations in postmenopausal women with resected early stage breast cancer. Citation Format: {Authors}. {Abstract title} [abstract]. In: Proceedings of the AACR-NCI-EORTC International Conference: Molecular Targets and Cancer Therapeutics; 2011 Nov 12-16; San Francisco, CA. Philadelphia (PA): AACR; Mol Cancer Ther 2011;10(11 Suppl):Abstract nr C114.

Abstract It is believed that multiple genetic variants with small individual effects contribute to the risk of alcohol dependence. Such polygenic effects are difficult to detect in genome-wide association studies that test for association of the phenotype with each single nucleotide polymorphism (SNP) individually. To overcome this challenge, gene-set analysis (GSA) methods that jointly test for the effects of pre-defined groups of genes have been proposed. Rather than testing for association between the phenotype and individual SNPs, these analyses evaluate the global evidence of association with a set of related genes enabling the identification of cellular or molecular pathways or biological processes that play a role in development of the disease. It is hoped that by aggregating the evidence of association for all available SNPs in a group of related genes, these approaches will have enhanced power to detect genetic associations with complex traits. We performed GSA using data from a genome-wide study of 1165 alcohol-dependent cases and 1379 controls from the Study of Addiction: Genetics and Environment (SAGE), for all 200 pathways listed in the Kyoto Encyclopedia of Genes and Genomes (KEGG) database. Results demonstrated a potential role of the ‘synthesis and degradation of ketone bodies’ pathway. Our results also support the potential involvement of the ‘neuroactive ligand–receptor interaction’ pathway, which has previously been implicated in addictive disorders. These findings demonstrate the utility of GSA in the study of complex disease, and suggest specific directions for further research into the genetic architecture of alcohol dependence.

With new technologies, multiple types of genomic data are commonly collected on a single set of samples. However, standard analysis methods concentrate on a single data type at a time and ignore the relationships between genes, proteins, and biochemical reactions that give rise to complex phenotypes. In this paper, we propose a novel integrative model to incorporate multiple types of genomic data into an association analysis with a complex phenotype. The method combines path analysis and stochastic search variable selection into a Bayesian hierarchical model that simultaneously identifies both direct and indirect genomic effects on the phenotype. Results from a simulation study and application of the Bayesian model to a pharmacogenomic study of the drug gemcitabine demonstrate greater sensitivity to detect genomic effects in some simulation scenarios, when compared to the standard single data type analysis. Further research is required to extend and modify this integrative modeling framework to increase computational efficiency to best capitalize on the wealth of information available across multiple genomic data types. Genet. Epidemiol. 36:352–359, 2012. © 2012 Wiley Periodicals, Inc.

Abstract Background: Genome-wide association studies (GWAS) for epithelial ovarian cancer (EOC), the most lethal gynecologic malignancy, have identified novel susceptibility loci. GWAS for survival after EOC have had more limited success. The association of each single-nucleotide polymorphism (SNP) individually may not be well suited to detect small effects of multiple SNPs, such as those operating within the same biologic pathway. Gene set analysis (GSA) overcomes this limitation by assessing overall evidence for association of a phenotype with all measured variation in a set of genes. Methods: To determine gene sets associated with EOC overall survival, we conducted GSA using data from two large GWAS (N cases = 2,813, N deaths = 1,116), with a novel Principal Component-Gamma GSA method. Analysis was completed for all cases and then separately for high-grade serous histologic subtype. Results: Analysis of the high-grade serous subjects resulted in 43 gene sets with P < 0.005 (1.7%); of these, 21 gene sets had P < 0.10 in both GWAS, including intracellular signaling pathway (P = 7.3 × 10−5) and macrolide binding (P = 6.2 × 10−4) gene sets. The top gene sets in analysis of all cases were meiotic mismatch repair (P = 6.3 × 10−4) and macrolide binding (P = 1.0 × 10−3). Of 18 gene sets with P < 0.005 (0.7%), eight had P < 0.10 in both GWAS. Conclusion: This research detected novel gene sets associated with EOC survival. Impact: Novel gene sets associated with EOC survival might lead to new insights and avenues for development of novel therapies for EOC and pharmacogenomic studies. Cancer Epidemiol Biomarkers Prev; 21(3); 529–36. ©2012 AACR.

AbstractGene‐set analyses have been widely used in gene expression studies, and some of the developed methods have been extended to genome wide association studies (GWAS). Yet, complications due to linkage disequilibrium (LD) among single nucleotide polymorphisms (SNPs), and variable numbers of SNPs per gene and genes per gene‐set, have plagued current approaches, often leading to ad hoc “fixes.” To overcome some of the current limitations, we developed a general approach to scan GWAS SNP data for both gene‐level and gene‐set analyses, building on score statistics for generalized linear models, and taking advantage of the directed acyclic graph structure of the gene ontology when creating gene‐sets. However, other types of gene‐set structures can be used, such as the popular Kyoto Encyclopedia of Genes and Genomes (KEGG). Our approach combines SNPs into genes, and genes into gene‐sets, but assures that positive and negative effects of genes on a trait do not cancel. To control for multiple testing of many gene‐sets, we use an efficient computational strategy that accounts for LD and provides accurate step‐down adjustedP‐values for each gene‐set. Application of our methods to two different GWAS provide guidance on the potential strengths and weaknesses of our proposed gene‐set analyses.

Abstract Purpose: Inherited variability in the prognosis of lung cancer patients treated with platinum-based chemotherapy has been widely investigated. However, the overall contribution of genetic variation to platinum response is not well established. To identify novel candidate single nucleotide polymorphisms (SNP)/genes, we carried out a genome-wide association study (GWAS) for cisplatin cytotoxicity by using lymphoblastoid cell lines (LCL), followed by an association study of selected SNPs from the GWAS with overall survival (OS) in lung cancer patients. Experimental Design: A GWAS for cisplatin was conducted with 283 ethnically diverse LCLs. A total of 168 top SNPs were genotyped in 222 small cell lung cancer (SCLC) and 961 non-SCLC (NSCLC) patients treated with platinum-based therapy. Association of the SNPs with OS was determined by using the Cox regression model. Selected candidate genes were functionally validated by siRNA knockdown in human lung cancer cells. Results: Among 157 successfully genotyped SNPs, 9 and 10 SNPs were top SNPs associated with OS for patients with NSCLC and SCLC, respectively, although they were not significant after adjusting for multiple testing. Fifteen genes, including 7 located within 200 kb up or downstream of the 4 top SNPs and 8 genes for which expression was correlated with 3 SNPs in LCLs were selected for siRNA screening. Knockdown of DAPK3 and METTL6, for which expression levels were correlated with the rs11169748 and rs2440915 SNPs, significantly decreased cisplatin sensitivity in lung cancer cells. Conclusions: This series of clinical and complementary laboratory-based functional studies identified several candidate genes/SNPs that might help predict treatment outcomes for platinum-based therapy of lung cancer. Clin Cancer Res; 17(17); 5801–11. ©2011 AACR.

Abstract Background Taxane, a class of common anticancer agent, is widely used to treat many cancers. Among the taxanes, paclitaxel and docetaxel are the most commonly used agents. The mechanism of action for taxane drugs is through stabilizing microtubules by enhancing tubulin assembly and against depolymerization, thereby disrupting the dynamics of the microtubule. Large inter-individual variation has been observed in response to taxane treatment. Therefore, in the present study, we conducted a genome-wide association (GWA) study to identify genetic polymorphisms that might contribute to variation in response to taxanes drugs using the “Human Variation Panel” lymphoblastoid cell line (LCL) model system that consists of 287 ethnically defined cell lines. Method Genome-wide SNP and basal gene expression data were obtained for 287 LCLs using Illumina HumanHap 550K, 510S BeadChips, Affymetix SNP Array 6.0 and Affymetrix U133 plus 2.0 GeneChip. Cytotoxicity assay was also performed in the same cell lines for both paclitaxel and docetaxel. Candidate genes were selected based on an integrated analysis of genome-wide SNPs, expression array data and IC50 values (a cytotoxicity phenotype indicating the drug concentration required for 50% inhibition of maximal cell growth) for paclitaxel and docetaxel. SiRNA screening was performed to functionally validate those top candidates, followed by MTS assay in a breast cancer cell line, MDA-MB231. Results Of the 54,000 probe sets considered, 390 probe sets, representing 272 unique annotated genes, were found to be associated with docetaxel cytotoxicity (IC50 value) with p value <10−3, while 176 probe sets, representing 124 unique genes, were associated with paclitaxel IC50. Of these probe sets, 50 probe sets were associated with IC50 for both drugs. Analysis of 1.3 million SNPs identified 11 loci, defined as a region containing at least 1 SNP of p value <10−5 and 1 SNP of p value <10−4 within 50kb, associated with docetaxel IC50, while 7 loci were associated with paclitaxel IC50. One locus was found to be associated with both drugs. Previous studies suggested that gene expression is regulated in a cell-type specific manner. Therefore, based on our analysis and biological implication, we selected 16 top candidate genes that were expressed in our LCLs to perform functional validation studies using siRNA, followed by MTS assay in a breast cancer cell line, MDA-MB231. Functional studies showed that knockdown of SLC44A1 and HSPA5 significantly desensitized the cancer cell to both taxanes treatment. Conclusion GWAS using LCL model system could provide potential pharmacogenomic candidates for future clinical translational studies. Functional validation in tumor cell lines may help determine the genomic mechanism contributing to the inter-individual variation of taxane response. Citation Format: {Authors}. {Abstract title} [abstract]. In: Proceedings of the 101st Annual Meeting of the American Association for Cancer Research; 2010 Apr 17-21; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2010;70(8 Suppl):Abstract nr 2599.

Abstract Background Radiation therapy is used to treat half of the all cancer patients. Response to radiation therapy varies widely, in terms of both response and toxicity related events. Previous studies suggested almost 80% of the variation in normal tissue response to radiation treatment was likely to be due to genetic factors. Therefore, we performed a genome-wide association study (GWAS) to identify biomarkers that might help predict radiation response using a the “Human Variation Panel” lymphoblastoid cell lines (LCLs) that consists 287 ethnically defined cell lines. Method Basal gene expression level, 1.3 million genome-wide SNP data were obtained for 287 human LCLs using Affymetrix U133 plus 2.0 GeneChip, Illumina HumanHap 550K, 510S BeadChips and Affymetix SNP Array 6.0. Radiation cytotoxicity assay was also performed to obtain the area under radiation dose response curve (AUC), a summary measure for cytotoxicity for the same cell lines. Functional validation of candidate genes, selected based on the integrated analysis of SNP, expression and AUC data, was performed in multiple cancer cell lines using specific siRNAs followed by MTS and colony formation assays. Results 27 loci, containing at least 2 SNPs of p values <10−4 within 50kb were associated with radiation AUC. 270 expression probe sets were associated with radiation AUC (p< 10−3). The integrated analysis of 27 loci, 54,000 basal expression probe sets and radiation AUC identified 50 unique SNPs associated with AUC were also associated with gene expression of 39 unique annotated genes. In addition, these 39 genes were associated with radiation AUC with p values <10−3. Based on the GWAS and integrated analysis of SNP, expression array and radiation AUC data, 22 genes were selected for functional validation studies using siRNA knock down followed by MTS and colony formation assays in multiple tumor cell lines. C13orf34 (Bora), MAD2L1, PLK4, TPD52 significantly altered the radiation sensitivity in at least 2 cancer cell lines for both MTS and colony formation assays (p<0.05). Knock down of DEPDC1B, a gene containing 2 SNPs at the 3′-end that was significantly related with Bora gene expression level (p<10−4) significantly desensitized cancer cells to radiation treatment. Both microarray data and QRT-PCR analysis indicated that expression levels for those two genes were significantly correlated. Conclusion Our results using the lymphoblastoid cell lines might help identify novel biomarkers that contribute to variation in response to radiation therapy and help understand the mechanisms underlying the association. Citation Format: {Authors}. {Abstract title} [abstract]. In: Proceedings of the 101st Annual Meeting of the American Association for Cancer Research; 2010 Apr 17-21; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2010;70(8 Suppl):Abstract nr 3602.