Strategies for Enriching Variant Coverage in Candidate Disease Loci on a Multiethnic Genotyping Array

Investigating genetic architecture of complex traits in ancestrally diverse populations is imperative to understand the etiology of disease. However, the current paucity of genetic research in people of African and Latin American ancestry, Hispanic and indigenous peoples in the United States is likely to exacerbate existing health disparities for many common diseases. The Population Architecture using Genomics and Epidemiology, Phase II (PAGE II), Study was initiated in 2013 by the National Human Genome Research Institute to expand our understanding of complex trait loci in ethnically diverse and well characterized study populations. To meet this goal, the Multi-Ethnic Genotyping Array (MEGA) was designed to substantially improve fine-mapping and functional discovery by increasing variant coverage across multiple ethnicities at known loci for metabolic, cardiovascular, renal, inflammatory, anthropometric, and a variety of lifestyle traits. Studying the frequency distribution of clinically relevant mutations, putative risk alleles, and known functional variants across multiple populations will provide important insight into the genetic architecture of complex diseases and facilitate the discovery of novel, sometimes population-specific, disease associations. DNA samples from 51,650 self-identified African ancestry (17,328), Hispanic/Latino (22,379), Asian/Pacific Islander (8,640), and American Indian (653) and an additional 2,650 participants of either South Asian or European ancestry, and other reference panels have been genotyped on MEGA by PAGE II. MEGA was designed as a new resource for studying ancestrally diverse populations. Here, we describe the methodology for selecting trait-specific content for use in multi-ethnic populations and how enriching MEGA for this content may contribute to deeper biological understanding of the genetic etiology of complex disease.

SOS2 and ACP1 Loci Identified through Large-Scale Exome Chip Analysis Regulate Kidney Development and Function

Genome-wide association studies have identified >50 common variants associated with kidney function, but these variants do not fully explain the variation in eGFR. We performed a two-stage meta-analysis of associations between genotypes from the Illumina exome array and eGFR on the basis of serum creatinine (eGFRcrea) among participants of European ancestry from the CKDGen Consortium (n_Stage1: 111,666; n_Stage2: 48,343). In single-variant analyses, we identified single nucleotide polymorphisms at seven new loci associated with eGFRcrea (PPM1J, EDEM3, ACP1, SPEG, EYA4, CYP1A1, and ATXN2L; P_Stage1<3.7×10^-7), of which most were common and annotated as nonsynonymous variants. Gene-based analysis identified associations of functional rare variants in three genes with eGFRcrea, including a novel association with the SOS Ras/Rho guanine nucleotide exchange factor 2 gene, SOS2 (P=5.4×10^-8 by sequence kernel association test). Experimental follow-up in zebrafish embryos revealed changes in glomerular gene expression and renal tubule morphology in the embryonic kidney of acp1- and sos2-knockdowns. These developmental abnormalities associated with altered blood clearance rate and heightened prevalence of edema. This study expands the number of loci associated with kidney function and identifies novel genes with potential roles in kidney formation.

Three new pancreatic cancer susceptibility signals identified on chromosomes 1q32.1, 5p15.33 and 8q24.21

Genome-wide association studies (GWAS) have identified common pancreatic cancer susceptibility variants at 13 chromosomal loci in individuals of European descent. To identify new susceptibility variants, we performed imputation based on 1000 Genomes (1000G) Project data and association analysis using 5,107 case and 8,845 control subjects from 27 cohort and case-control studies that participated in the PanScan I-III GWAS. This analysis, in combination with a two-staged replication in an additional 6,076 case and 7,555 control subjects from the PANcreatic Disease ReseArch (PANDoRA) and Pancreatic Cancer Case-Control (PanC4) Consortia uncovered 3 new pancreatic cancer risk signals marked by single nucleotide polymorphisms (SNPs) rs2816938 at chromosome 1q32.1 (per allele odds ratio (OR) = 1.20, P = 4.88x10 -15), rs10094872 at 8q24.21 (OR = 1.15, P = 3.22x10 -9) and rs35226131 at 5p15.33 (OR = 0.71, P = 1.70x10 -8). These SNPs represent independent risk variants at previously identified pancreatic cancer risk loci on chr1q32.1 ( NR5A2), chr8q24.21 ( MYC) and chr5p15.33 ( CLPTM1L- TERT) as per analyses conditioned on previously reported susceptibility variants. We assessed expression of candidate genes at the three risk loci in histologically normal ( n = 10) and tumor ( n = 8) derived pancreatic tissue samples and observed a marked reduction of NR5A2 expression (chr1q32.1) in the tumors (fold change -7.6, P = 5.7x10 -8). This finding was validated in a second set of paired ( n = 20) histologically normal and tumor derived pancreatic tissue samples (average fold change for three NR5A2 isoforms -31.3 to -95.7, P = 7.5x10 -4-2.0x10 -3). Our study has identified new susceptibility variants independently conferring pancreatic cancer risk that merit functional follow-up to identify target genes and explain the underlying biology.

A reference panel of 64,976 haplotypes for genotype imputation

We describe a reference panel of 64,976 human haplotypes at 39,235,157 SNPs constructed using whole-genome sequence data from 20 studies of predominantly European ancestry. Using this resource leads to accurate genotype imputation at minor allele frequencies as low as 0.1% and a large increase in the number of SNPs tested in association studies, and it can help to discover and refine causal loci. We describe remote server resources that allow researchers to carry out imputation and phasing consistently and efficiently.

Association between Adult Height and Risk of Colorectal, Lung, and Prostate Cancer: Results from Meta-analyses of Prospective Studies and Mendelian Randomization Analyses

BACKGROUND

Observational studies examining associations between adult height and risk of colorectal, prostate, and lung cancers have generated mixed results. We conducted meta-analyses using data from prospective cohort studies and further carried out Mendelian randomization analyses, using height-associated genetic variants identified in a genome-wide association study (GWAS), to evaluate the association of adult height with these cancers.

METHODS AND FINDINGS

A systematic review of prospective studies was conducted using the PubMed, Embase, and Web of Science databases. Using meta-analyses, results obtained from 62 studies were summarized for the association of a 10-cm increase in height with cancer risk. Mendelian randomization analyses were conducted using summary statistics obtained for 423 genetic variants identified from a recent GWAS of adult height and from a cancer genetics consortium study of multiple cancers that included 47,800 cases and 81,353 controls. For a 10-cm increase in height, the summary relative risks derived from the meta-analyses of prospective studies were 1.12 (95% CI 1.10, 1.15), 1.07 (95% CI 1.05, 1.10), and 1.06 (95% CI 1.02, 1.11) for colorectal, prostate, and lung cancers, respectively. Mendelian randomization analyses showed increased risks of colorectal (odds ratio [OR] = 1.58, 95% CI 1.14, 2.18) and lung cancer (OR = 1.10, 95% CI 1.00, 1.22) associated with each 10-cm increase in genetically predicted height. No association was observed for prostate cancer (OR = 1.03, 95% CI 0.92, 1.15). Our meta-analysis was limited to published studies. The sample size for the Mendelian randomization analysis of colorectal cancer was relatively small, thus affecting the precision of the point estimate.

CONCLUSIONS

Our study provides evidence for a potential causal association of adult height with the risk of colorectal and lung cancers and suggests that certain genetic factors and biological pathways affecting adult height may also affect the risk of these cancers.

Cross-Cancer Genome-Wide Analysis of Lung, Ovary, Breast, Prostate, and Colorectal Cancer Reveals Novel Pleiotropic Associations

Identifying genetic variants with pleiotropic associations can uncover common pathways influencing multiple cancers. We took a two-stage approach to conduct genome-wide association studies for lung, ovary, breast, prostate, and colorectal cancer from the GAME-ON/GECCO Network (61,851 cases, 61,820 controls) to identify pleiotropic loci. Findings were replicated in independent association studies (55,789 cases, 330,490 controls). We identified a novel pleiotropic association at 1q22 involving breast and lung squamous cell carcinoma, with eQTL analysis showing an association with ADAM15/THBS3 gene expression in lung. We also identified a known breast cancer locus CASP8/ALS2CR12 associated with prostate cancer, a known cancer locus at CDKN2B-AS1 with different variants associated with lung adenocarcinoma and prostate cancer, and confirmed the associations of a breast BRCA2 locus with lung and serous ovarian cancer. This is the largest study to date examining pleiotropy across multiple cancer-associated loci, identifying common mechanisms of cancer development and progression. Cancer Res; 76(17); 5103-14. ©2016 AACR.

Ovarian Cancer Risk Factors by Histologic Subtype: An Analysis From the Ovarian Cancer Cohort Consortium

PURPOSE

An understanding of the etiologic heterogeneity of ovarian cancer is important for improving prevention, early detection, and therapeutic approaches. We evaluated 14 hormonal, reproductive, and lifestyle factors by histologic subtype in the Ovarian Cancer Cohort Consortium (OC3).

PATIENTS AND METHODS

Among 1.3 million women from 21 studies, 5,584 invasive epithelial ovarian cancers were identified (3,378 serous, 606 endometrioid, 331 mucinous, 269 clear cell, 1,000 other). By using competing-risks Cox proportional hazards regression stratified by study and birth year and adjusted for age, parity, and oral contraceptive use, we assessed associations for all invasive cancers by histology. Heterogeneity was evaluated by likelihood ratio test.

RESULTS

Most risk factors exhibited significant heterogeneity by histology. Higher parity was most strongly associated with endometrioid (relative risk [RR] per birth, 0.78; 95% CI, 0.74 to 0.83) and clear cell (RR, 0.68; 95% CI, 0.61 to 0.76) carcinomas (P value for heterogeneity [P-het] < .001). Similarly, age at menopause, endometriosis, and tubal ligation were only associated with endometrioid and clear cell tumors (P-het ≤ .01). Family history of breast cancer (P-het = .008) had modest heterogeneity. Smoking was associated with an increased risk of mucinous (RR per 20 pack-years, 1.26; 95% CI, 1.08 to 1.46) but a decreased risk of clear cell (RR, 0.72; 95% CI, 0.55 to 0.94) tumors (P-het = .004). Unsupervised clustering by risk factors separated endometrioid, clear cell, and low-grade serous carcinomas from high-grade serous and mucinous carcinomas.

CONCLUSION

The heterogeneous associations of risk factors with ovarian cancer subtypes emphasize the importance of conducting etiologic studies by ovarian cancer subtypes. Most established risk factors were more strongly associated with nonserous carcinomas, which demonstrate challenges for risk prediction of serous cancers, the most fatal subtype.

Common variants in the obesity-associated genes FTO and MC4R are not associated with risk of colorectal cancer

BACKGROUND

Obesity is a convincing risk factor for colorectal cancer. Genetic variants in or near FTO and MC4R are consistently associated with body mass index and other body size measures, but whether they are also associated with colorectal cancer risk is unclear.

METHODS

In the discovery stage, we tested associations of 677 FTO and 323 MC4R single nucleotide polymorphisms (SNPs) 100kb upstream and 300kb downstream from each respective locus with risk of colorectal cancer in data from the Colon Cancer Family Registry (CCFR: 1960 cases; 1777 controls). Next, all SNPs that were nominally statistically significant (p<0.05) in the discovery stage were included in replication analyses in data from the Genetics and Epidemiology of Colorectal Cancer Consortium (GECCO: 9716 cases; 9844 controls).

RESULTS

In the discovery stage, 43 FTO variants and 18 MC4R variants were associated with colorectal cancer risk (p<0.05). No SNPs remained statistically significant in the replication analysis after accounting for multiple comparisons.

CONCLUSION

We found no evidence that individual variants in or near the obesity-related genes FTO and MC4R are associated with risk of colorectal cancer.

Abstract 136: Refining the molecular profile of colorectal tumors to expand prevention and treatment opportunities

The completion of The Cancer Genome Atlas (TCGA) project for colorectal cancer (CRC) is ushering in a new phase of identifying treatment strategies tailored to the molecular profile of each person's tumor. Precision medicine approaches to cancer treatment rely on the identification of molecular profiles that can be used to identify effective therapies and can be used in a targeted sequencing setting to make treatment decisions.

The initial TCGA colorectal effort included 276 samples and focused on integrating data from exome sequencing with genome-wide DNA copy number alterations (CNAs), DNA methylation, and mRNA and microRNA expression. Since then a total of 626 samples have been completed with the potential to refine CRC subtypes, identify novel mutated pathways, and further functional understanding. Such a large data set presents opportunities to identify new recurrent drug targets and to stratify patients into groups that are predictive of treatment response. However, large data sets also present substantial challenges, since hand-curation becomes intractable, while computational tools can be overwhelmed by hypermutation and copy number changes.

Here we present a comprehensive molecular analysis of all 626 TCGA colorectal cancer samples, including exome sequencing, CNAs, DNA methylation, and mRNA expression. For each data type, we identified recurrently altered genes. Using MutSigCV on 525 samples yielded 27 and 87 significantly mutated genes in non-hypermutated and hypermutated samples, respectively, a substantial increase over the 15 and 17 somatically recurrently mutated genes identified using MutSig in non-hypermutated and hypermutated samples, respectively, in the previously published TCGA colorectal study. For example, PTEN, a known tumor suppressor, was not reported as significantly recurrently mutated in the initial TCGA non-hypermutated set; however, it was in the larger non-hypermutated set, demonstrating the power of a larger data set for assessing the significance and relative frequency of mutations in the context of known subtypes.

In addition, we integrated the somatic mutation data, copy number data, LOH data, and hyper-methylation data to identify genes, like MLH1, that are recurrently disrupted by different mechanisms. We also considered somatic mutations that are likely gain-of-function mutations based on nonrandom clustering; and we used recurrent indels to identify loss-of-function drivers in samples positive for microsatellite instability (MSI). We further classified each sample using the previously identified subtypes – BRAF+, KRAS+, APC+, CTNNB1+ (beta-catenin+), TGFBR2/SMAD4+, PTEN+ and PIK3CA+, and R-spondin fusion positive, as well as CpG Island Methylator Phenotype (CIMP) and MSI - in order to refine the relevant molecular signatures driving CRC etiology and thereby prevention and treatment paradigms.

Citation Format: Catherine S. Grasso, Eve Shinbrot, Ming Yu, Max Liesersen, Mark Chaisson, Andrew Chan, Charles Connolly, James Dai, Margaret Du, Charles Fuchs, Levi Garraway, Marios Giannakis, Tabitha Harrison, Li Hsu, Jeroen Huyghe, Jasmine Mu, Shuji Ogino, Colin Pritchard, Stephen Salipante, Wei Sun, Syed H. Zaidi, Ni Zhao, William Grady, Ben Raphael, Thomas Hudson, David Wheeler, Ulrike Peters. Refining the molecular profile of colorectal tumors to expand prevention and treatment opportunities. [abstract]. In: Proceedings of the 107th Annual Meeting of the American Association for Cancer Research; 2016 Apr 16-20; New Orleans, LA. Philadelphia (PA): AACR; Cancer Res 2016;76(14 Suppl):Abstract nr 136.

Abstract 5221: Linking the molecular profile of colorectal tumors to germline genetic and environmental risk factors

The completion of The Cancer Genome Atlas (TCGA) project for colorectal cancer (CRC) has enabled a new, focused phase of sequencing tumor samples for which genome-wide genetic, epidemiological, clinical and lifestyle data have been collected. By combining somatic mutational profiles with these aforementioned data, we may identify and report effective prevention and treatment approaches for a broader population of individuals. The advent of targeted deep sequencing approaches using DNA obtained from formalin fixed paraffin embedded tissues has made possible the genetic characterization of the large numbers of patients needed to make such an effort relevant.

As a first step, we describe a custom gene panel designed from large-scale studies for targeted deep sequencing, and its application to over 4,200 CR tumors, collected by the Genetics and Epidemiology of Colorectal Cancer Consortium (GECCO). This study is designed to identify recurrent somatic mutations and copy number alterations (CNAs) for association testing with germline genetic and lifestyle and environmental risk factors for CRC, and thereby identify relevant approaches to impact cancer prevention.

The targeted CRC panel includes 205 genes. Genes were primarily selected as significantly mutated genes identified from the Nurses’ Health Study and Health Professional's Follow-up Study (n = 700), and TCGA (n = 525). We also included 15 genes associated with high penetrance germline mutations and augmented the list to include genes in pathways of somatically altered genes, identified by literature review, from public databases and known to be associated with loss of heterozygosity. For these 205 genes, amplification primers were designed to include all coding regions of transcripts that are listed in the UCSC Genome Browser.

For regions with CNAs, the TCGA dataset was analyzed to include regions with greater than or equal to 2 copy focal gains or losses that were found in more than 4 or 3 tumors, respectively. Candidate genes in regions with significant CNAs from the TCGA CRC publication (Nature 2012) were also included. For CNAs, 6 to 12 amplicons were designed for each of the 56 selected regions (32 gains and 24 losses).

Additional target regions include: 1) 25 microsatellite loci previously used to identify defective DNA mismatch repair and 212 homoploymer repeats. These include microsatellite loci recommended by the NCI Consensus Panel for identifying MSI; 2) amelogenin (for gender); and 3) Fusobacterium to detect a putative CRC-associated pathogen in tumor biopsies.

At the AACR annual meeting, we expect to present results for deep sequencing (∼1,000X) of the first 1,000 CR tumors, including any preliminarily identified pathways and subtypes that may provide the basis for association testing with germline genetic and lifestyle and environmental risk factors needed for inferring better approaches to prevention and treatment of CRC.

Citation Format: Syed H. Zaidi, Catie Grasso, Jasmine Mu, Eve Shinbrot, Marios Giannakis, Charles Connolly, Ivan Borozan, Hermann Brenner, Peter Campbell, Andrew Chan, Jenny Chang-Claude, Mengmeng Du, Vincent Ferretti, Amy French, Charles Fuchs, Steven Gallinger, Levi Garraway, Andrea Gsur, Marc Gunter, Tabitha Harrison, Michael Hoffmeister, Li Hsu, Wen-Yi Huang, Jeroen Huyghe, Mathieu Lemire, Elaine Mardis, John McPherson, Polly Newcomb, Lincoln Stein, Wei Sun, Lee Timms, Quang Trinh, David Wheeler, Christina Yung, Niha Zubair, Shuji Ogino, Stephen Thibodeau, Ulrike Peters, Thomas Hudson. Linking the molecular profile of colorectal tumors to germline genetic and environmental risk factors. [abstract]. In: Proceedings of the 107th Annual Meeting of the American Association for Cancer Research; 2016 Apr 16-20; New Orleans, LA. Philadelphia (PA): AACR; Cancer Res 2016;76(14 Suppl):Abstract nr 5221.

Abstract LB-365: Novel susceptibility loci for colorectal cancer: Findings from the colorectal transdisciplinary (CORECT) study OncoArray analysis

Genome-wide association studies (GWAS) have identified 58 susceptibility alleles across 37 regions associated with the incidence of colorectal cancer (CRC) with P&lt;5E-08, yet much of the disease's familial risk remains to be explained. In its first phase, the Colorectal Transdisciplinary (CORECT) study, in collaboration with the Genetics and Epidemiology of Colorectal Cancer Consortium (GECCO), the Colon Cancer Family Registry (CCFR), the Asian Colorectal Cancer Consortium (ACCC) and the Multiethnic Cohort (MEC), identified 6 of these loci based on 18,299 cases and 19,656 controls. The CORECT consortium recently completed genotyping on approximately 26,000 cases and 18,000 controls using the Illumina Infinium® OncoArray platform with the goals of 1) identifying novel overall and ethnic-specific susceptibility alleles and 2) fine-mapping known CRC risk loci. Newly genotyped samples represent multiple ethnic groups and include individuals primarily of European (75%), Asian (19%) and Hispanic (2.5%) origins. Combining both phases of CORECT, this study constitutes the largest GWAS meta-analysis of CRC to date. Here, we present European-specific results derived from genetically-defined Europeans genotyped on the OncoArray (16,456 cases and 10,442 controls), CORECT Phase I (5,584 cases and 5,329 controls), and CCFR/GECCO (12,715 cases and 14,327 controls), for a total of 34,755 cases and 30,098 controls. OncoArray genotype data were imputed to the 1000 Genomes Phase III reference panel, and summary results from logistic regression adjusting for age, sex, global ancestry, and study-specific covariates were combined in a fixed-effects inverse variance-weighted meta-analysis. Preliminary results indicate at least 10 new low-penetrance risk variants that reach genome-wide significance (P&lt;5E-08) and that are independent of known risk loci. Further, fine-mapping of well-established CRC susceptibility regions is underway. This investigation provides additional insight into the etiology of CRC and informs future risk modeling efforts.

Citation Format: Stephanie L. Schmit, Fredrick R. Schumacher, Christopher K. Edlund, Jian Gong, Gad Rennert, Wei Zheng, Loic Le Marchand, Ulrike Peters, Graham Casey, Li Hsu, Stephen B. Gruber, David V. Conti, CORECT, GECCO, CCFR, ACCC, and MEC. Novel susceptibility loci for colorectal cancer: Findings from the colorectal transdisciplinary (CORECT) study OncoArray analysis. [abstract]. In: Proceedings of the 107th Annual Meeting of the American Association for Cancer Research; 2016 Apr 16-20; New Orleans, LA. Philadelphia (PA): AACR; Cancer Res 2016;76(14 Suppl):Abstract nr LB-365.

Abstract 2587: Comprehensive colorectal cancer risk prediction to inform personalized screening and intervention

Colorectal cancer (CRC) is the second leading cause of cancer deaths in the United States, despite the fact that it is one of the most preventable and treatable cancers when detected early via screening. The current screening guidelines for CRC are based on age, family history of CRC, and previous screening results. However, multiple environmental and lifestyle risk factors have been established or suspected for CRC, as have many common genetic susceptibility loci. It is critical to utilize this information to better stratify individuals into low- and high-risk groups for optimized and personalized screening and intervention recommendations.

Using data from two large consortia (8421 CRC cases and 9767 controls): the Genetics and Epidemiology of Colorectal Cancer Consortium (GECCO) and the Colorectal Transdisciplinary study (CORECT), we developed risk prediction models for men and women based on family history, environmental and lifestyle risk factors, and known CRC susceptibility loci identified through genome-wide association studies. We constructed an environmental risk score (E-score) as a weighted sum of 19 established or potential environmental and lifestyle risk factors for CRC with weights obtained from a multivariate logistic regression analysis. Compared to the model that includes only family history, the E-score significantly improves the discriminatory accuracy for both men (AUC = 0.62 vs. 0.53, p-value &lt; 1e-5) and women (AUC = 0.60 vs. 0.52, p-value &lt; 1e-5). Similarly, we also constructed a genetic risk score (G-score) using 50 common variants associated with CRC risk, and the G-score also significantly improves the discriminatory accuracy for both men (AUC = 0.60 vs. 0.53, p-value &lt; 1e-5) and women (AUC = 0.59 vs. 0.52, p-value &lt; 1e-5) over the family history-only model. Compared to the model with family history and E-score, the inclusion of the G-score in the model further improves the discriminatory accuracy for both men (AUC = 0.65 vs. 0.62, p-value = 0.0152) and women (AUC = 0.63 vs. 0.60, p-value = 0.0005).

Our risk prediction models are the first to incorporate both comprehensive environmental and lifestyle risk factors, and known CRC common genetic variants. The E- and G-scores are independent risk predictors for CRC, and models that incorporate both scores improve the discriminatory accuracy significantly compared to family history-only models. Using risk-factor distributions available from nationally representative data (e.g., NHANES), we will provide absolute-risk estimates of CRC using both the E- and G-scores. We expect our comprehensive models incorporating both environmental and genetic risk factors to provide more accurate estimation of CRC, which will be useful for recommending individually tailored screening and intervention strategies to prevent this common cancer.

Citation Format: Jihyoun Jeon, Sonja I. Berndt, Hermann Brenner, Peter T. Campbell, Andrew T. Chan, Jenny Chang-Claude, Mengmeng Du, Graham Giles, Jian Gong, Stephen B. Gruber, Tabitha A. Harrison, Michael Hoffmeister, Loic LeMarchand, Li Li, John D. Potter, Gad Rennert, Robert E. Schoen, Martha L. Slattery, Emily White, Michael O. Woods, Ulrike Peters, Li Hsu. Comprehensive colorectal cancer risk prediction to inform personalized screening and intervention. [abstract]. In: Proceedings of the 107th Annual Meeting of the American Association for Cancer Research; 2016 Apr 16-20; New Orleans, LA. Philadelphia (PA): AACR; Cancer Res 2016;76(14 Suppl):Abstract nr 2587.

Abstract 5230: Large scale whole genome sequencing with imputation into GWAS improves our understanding of the genetic architecture of colorectal cancer

Whole-genome sequencing (WGS) has started a new era in human genetics in which data can be used to more fully understand the role of genetic variation in common complex diseases, including the role of less frequent and rare variants and structural variation. To explore the impact of these variants on colorectal cancer risk we conducted the first large scale WGS study for colorectal cancer (CRC) including 1,961 CRC cases and 981 controls. These WGS data as well as those from the Haplotype Reference Consortium were imputed in 13,104 CRC cases and 15,521 controls with genome-wide association study (GWAS) data that are part of the Colorectal Cancer Family Registry (CCFR) and the Genetics and Epidemiology of Colorectal Cancer Consortium (GECCO). Focusing on rare and less frequent variants, insertions and deletions we observed potentially novel variants: a less frequent variant (MAF = 0.026) on chromosome 5 located in NREP/STARD4-AS1 (p = value 4E-08); and a novel rare multi-allelic variant (MAF = 0.003) on chromosome 9 near KLF9 and TRPM3 (p-value 2E-09; the other allele of this multi-allelic variant had a MAF of 0.0003 and p-value of 0.55). Furthermore, we observed an independent locus close to the known region 8q24 that was located upstream of GSDMC (MAF = 0.16, p-value 5E-08). Within the known region 8q23/EIF3H we identified several low frequency variants with similar MAF (0.0181 to 0.0204) including a 6bp deletion with p-values between 4E-08 and 1E-09 that were independent of the common variant signal in this region. In addition, we identified statistically significant (p&lt;5E-08) deletions, insertions, and an essential splice site within known GWAS loci that present interesting candidates for functional studies. We will follow up these findings in independent samples from the Colorectal Cancer Transdisciplinary Study (CORECT) and CCFR, as well as additional samples currently genotyped in GECCO. In conclusion, next generation sequencing combined with imputation in large GWAS data sets has the potential to identify novel low frequency and rare genetic variants, aid fine-mapping of known CRC susceptibility loci and point to interesting functional candidates.

Citation Format: Jeroen Huyghe, Sai Chen, Hyun M. Kang, Tabitha A. Harrison, Sonja I. Berndt, Stephane Bézieau, Hermann Brenner, Graham Casey, Andrew T. Chan, Jenny Chang-Claude, Gallinger J. Steven, Stephen B. Gruber, Andrea Gsur, Michael Hoffmeister, Thomas J. Hudson, Loic Le Marchand, Polly A. Newcomb, John D. Potter, Conghui Qu, Martha L. Slattery, Joshua D. Smith, Emily White, Li Hsu, Goncalo R. Abecasis, Deborah A. Nickerson, Ulrike Peters. Large scale whole genome sequencing with imputation into GWAS improves our understanding of the genetic architecture of colorectal cancer. [abstract]. In: Proceedings of the 107th Annual Meeting of the American Association for Cancer Research; 2016 Apr 16-20; New Orleans, LA. Philadelphia (PA): AACR; Cancer Res 2016;76(14 Suppl):Abstract nr 5230.

Abstract 4284: Exploratory plasma proteomic analysis in a randomized cross-over trial of aspirin among healthy individuals

Background: Long-term use of aspirin is associated with lower colorectal cancer (CRC) incidence; however, the mechanism of the chemopreventive effect of aspirin is not fully understood. Animal studies suggest that COX-2, NFκB signaling and Wnt/β-catenin pathways may play a role, but no clinical trials have systematically evaluated the biological response to aspirin in healthy humans. Methods: We assessed the difference in plasma protein levels after 60 days of regular dose aspirin (325 mg/day) compared to placebo in a randomized, double-blinded, placebo-controlled, cross-over trial of 44 healthy non-smoking men and women, aged 21-45 years. Plasma proteomics was analyzed on an antibody microarray with ∼3,000 full-length antibodies, printed in triplicate. Moderated paired t-test was performed on individual antibodies, and gene set analyses were performed for KEGG and GO pathways. Results: Among the 3,387 antibodies, significant differences in plasma protein levels were observed for 267 antibodies (p&lt;0.05), the most significant protein being a transcription-factor regulator belonging to a steroid receptor family and found to be differentially expressed in colon cancer cells. Other significant proteins are involved in multiple oncogenic pathways related to colon tumorigenesis. In the pathway analysis, 4 KEGG (among 138) and 69 GO (among 1,089) pathways were found to be significant (p&lt;0.05), including natural killer (NK) cell-mediated cytotoxicity, butanoate metabolism and Wnt signaling pathways. Pathways that modulate cellular protein binding to steroid receptors were also significantly different between aspirin treatment and placebo (p&lt;0.05). None of the results remained statistically significant after correction for multiple testing. Conclusion: Several proteins and pathways, which have previously been reported as playing a role in colorectal carcinogenesis in vitro were found to be differentially expressed after aspirin treatment vs. placebo in healthy human subjects. This study suggests several chemopreventive mechanisms of aspirin; however, larger, confirmatory studies are needed.

Citation Format: Xiaoliang Wang, Yuzheng Zhang, Ali Shojaie, Paul D. Lampe, Lisa Levy, Ulrike Peters, John D. Potter, Emily White, Johanna W. Lampe. Exploratory plasma proteomic analysis in a randomized cross-over trial of aspirin among healthy individuals. [abstract]. In: Proceedings of the 107th Annual Meeting of the American Association for Cancer Research; 2016 Apr 16-20; New Orleans, LA. Philadelphia (PA): AACR; Cancer Res 2016;76(14 Suppl):Abstract nr 4284.

Abstract 4489: Using functional data from Roadmap Epigenomics to inform analysis of rare variants linked to gene expression in a large colorectal cancer study

To investigate the role of low frequency and rare genetic variation in colorectal cancer (CRC) susceptibility, the Genetics and Epidemiology of Colorectal Cancer Consortium (GECCO) and the Colorectal Cancer Family Registry (CCFR) conducted whole genome sequencing and imputed into genome-wide association studies (GWAS) of 14,718 CRC cases and 12,186 controls. These data provide a unique opportunity to investigate rare variants, which contribute to the majority of the variation in the genome. To improve power for discovering rare CRC susceptibility variants (&lt;1% MAF), Roadmap Epigenomics data were used to construct biologically relevant testing sets of enhancers, promoters and exons for gene-based association testing across the genome. Since enhancers exert their effects by impacting expression of target genes, we defined enhancer-gene networks by linking enhancer(s) to target gene expression using Roadmap chromatin state maps and gene expression. Variants in linked enhancers from digestive and immune tissues were aggregated together with variants in the promoter and non-synonymous coding variants in the target gene. We tested 9,884 variant sets for association with CRC risk using the Mixed effects Score Test (MiST). Our most significant findings are for acyl-Coenzyme A dehydrogenase, C-2 to C-3 short chain precursor-ACADS (p = 1×10−4), AlkB homologs, including AlkB homolog 1-ALKBH1 (p = 2×10−4), and SRA stem-loop interacting RNA binding protein-SLIRP (p = 2×10−4). We will replicate these findings within the Colorectal Cancer Transdisciplinary Study (CORECT), as well as additional samples currently genotyped in CCFR and GECCO (over 25,000 CRC cases and controls). Although the top findings are statistically non-significant in this initial dataset, each of these genes linked to molecular pathways implicated in CRC carcinogenesis (fatty acid metabolism, DNA/RNA repair, and Nuclear Receptor signaling pathway, which interacts with the Wnt, beta-catenin pathways to result in a diverse array of cellular effects including altered cellular adhesion, tissue morphogenesis, and oncogenesis). Our current findings suggest that although functional insight can improve power for novel discovery, even larger sample sizes and/or pathway-based analyses are necessary to understand the role of rare variants in CRC carcinogenesis.

Citation Format: Stephanie A. Bien, Tabitha A. Harrison, Paul L. Auer, Flora Qu, Jeroen Huyghe, Barbara Banbury, Peyton Greenside, Goncalo R. Abecasis, Sonja I. Berndt, Stephane Bézieau, Hermann Brenner, Graham Casey, Andrew T. Chan, Jenny Chang-Claude, Sai Chen, Joshua D. Smith, Loic Le Marchand, Christopher Carlson, Polly A. Newcomb, Christian Fuchsberger, Marty L. Slattery, Hyun M. Kang, Emily White, John Potter, Steven J. Gallinger, Michael Hoffmeister, Stephen B. Gruber, Deborah A. Nickerson, Ulrike Peters, Anshul Kundaje, Li Hsu. Using functional data from Roadmap Epigenomics to inform analysis of rare variants linked to gene expression in a large colorectal cancer study. [abstract]. In: Proceedings of the 107th Annual Meeting of the American Association for Cancer Research; 2016 Apr 16-20; New Orleans, LA. Philadelphia (PA): AACR; Cancer Res 2016;76(14 Suppl):Abstract nr 4489.

Fine-Mapping of Common Genetic Variants Associated with Colorectal Tumor Risk Identified Potential Functional Variants

Genome-wide association studies (GWAS) have identified many common single nucleotide polymorphisms (SNPs) associated with colorectal cancer risk. These SNPs may tag correlated variants with biological importance. Fine-mapping around GWAS loci can facilitate detection of functional candidates and additional independent risk variants. We analyzed 11,900 cases and 14,311 controls in the Genetics and Epidemiology of Colorectal Cancer Consortium and the Colon Cancer Family Registry. To fine-map genomic regions containing all known common risk variants, we imputed high-density genetic data from the 1000 Genomes Project. We tested single-variant associations with colorectal tumor risk for all variants spanning genomic regions 250-kb upstream or downstream of 31 GWAS-identified SNPs (index SNPs). We queried the University of California, Santa Cruz Genome Browser to examine evidence for biological function. Index SNPs did not show the strongest association signals with colorectal tumor risk in their respective genomic regions. Bioinformatics analysis of SNPs showing smaller P-values in each region revealed 21 functional candidates in 12 loci (5q31.1, 8q24, 11q13.4, 11q23, 12p13.32, 12q24.21, 14q22.2, 15q13, 18q21, 19q13.1, 20p12.3, and 20q13.33). We did not observe evidence of additional independent association signals in GWAS-identified regions. Our results support the utility of integrating data from comprehensive fine-mapping with expanding publicly available genomic databases to help clarify GWAS associations and identify functional candidates that warrant more onerous laboratory follow-up. Such efforts may aid the eventual discovery of disease-causing variant(s).

Female chromosome X mosaicism is age-related and preferentially affects the inactivated X chromosome

To investigate large structural clonal mosaicism of chromosome X, we analysed the SNP microarray intensity data of 38,303 women from cancer genome-wide association studies (20,878 cases and 17,425 controls) and detected 124 mosaic X events >2 Mb in 97 (0.25%) women. Here we show rates for X-chromosome mosaicism are four times higher than mean autosomal rates; X mosaic events more often include the entire chromosome and participants with X events more likely harbour autosomal mosaic events. X mosaicism frequency increases with age (0.11% in 50-year olds; 0.45% in 75-year olds), as reported for Y and autosomes. Methylation array analyses of 33 women with X mosaicism indicate events preferentially involve the inactive X chromosome. Our results provide further evidence that the sex chromosomes undergo mosaic events more frequently than autosomes, which could have implications for understanding the underlying mechanisms of mosaic events and their possible contribution to risk for chronic diseases.

It is unclear how often genetic mosaicism of chromosome X arises. Here, the authors examine women with cancer and cancer-free controls and show that X chromosome mosaicism occurs more frequently than on autosomes, especially on the inactive X chromosome, but is not linked to non-haematologic cancer risk

Mendelian randomization study of adiposity-related traits and risk of breast, ovarian, prostate, lung and colorectal cancer

BACKGROUND

Adiposity traits have been associated with risk of many cancers in observational studies, but whether these associations are causal is unclear. Mendelian randomization (MR) uses genetic predictors of risk factors as instrumental variables to eliminate reverse causation and reduce confounding bias. We performed MR analyses to assess the possible causal relationship of birthweight, childhood and adult body mass index (BMI), and waist-hip ratio (WHR) on the risks of breast, ovarian, prostate, colorectal and lung cancers.

METHODS

We tested the association between genetic risk scores and each trait using summary statistics from published genome-wide association studies (GWAS) and from 51 537 cancer cases and 61 600 controls in the Genetic Associations and Mechanisms in Oncology (GAME-ON) Consortium.

RESULTS

We found an inverse association between the genetic score for childhood BMI and risk of breast cancer [odds ratio (OR) = 0.71 per standard deviation (s.d.) increase in childhood BMI; 95% confidence interval (CI): 0.60, 0.80; P = 6.5 × 10(-5)). We also found the genetic score for adult BMI to be inversely associated with breast cancer risk (OR = 0.66 per s.d. increase in BMI; 95% CI: 0.57, 0.77; P = 2.5 × 10(-7)), and positively associated with ovarian cancer (OR = 1.35; 95% CI: 1.05, 1.72; P = 0.017), lung cancer (OR = 1.27; 95% CI: 1.09, 1.49; P = 2.9 × 10(-3)) and colorectal cancer (OR = 1.39; 95% CI: 1.06, 1.82, P = 0.016). The inverse association between genetically predicted adult BMI and breast cancer risk remained even after adjusting for directional pleiotropy via MR-Egger regression.

CONCLUSIONS

Findings from this study provide additional understandings of the complex relationship between adiposity and cancer risks. Our results for breast and lung cancer are particularly interesting, given previous reports of effect heterogeneity by menopausal status and smoking status.

Human genome meeting 2016 : Houston, TX, USA. 28 February - 2 March 2016

O1 The metabolomics approach to autism: identification of biomarkers for early detection of autism spectrum disorder A. K. Srivastava, Y. Wang, R. Huang, C. Skinner, T. Thompson, L. Pollard, T. Wood, F. Luo, R. Stevenson O2 Phenome-wide association study for smoking- and drinking-associated genes in 26,394 American women with African, Asian, European, and Hispanic descents R. Polimanti, J. Gelernter O3 Effects of prenatal environment, genotype and DNA methylation on birth weight and subsequent postnatal outcomes: findings from GUSTO, an Asian birth cohort X. Lin, I. Y. Lim, Y. Wu, A. L. Teh, L. Chen, I. M. Aris, S. E. Soh, M. T. Tint, J. L. MacIsaac, F. Yap, K. Kwek, S. M. Saw, M. S. Kobor, M. J. Meaney, K. M. Godfrey, Y. S. Chong, J. D. Holbrook, Y. S. Lee, P. D. Gluckman, N. Karnani, GUSTO study group O4 High-throughput identification of specific qt interval modulating enhancers at the SCN5A locus A. Kapoor, D. Lee, A. Chakravarti O5 Identification of extracellular matrix components inducing cancer cell migration in the supernatant of cultivated mesenchymal stem cells C. Maercker, F. Graf, M. Boutros O6 Single cell allele specific expression (ASE) IN T21 and common trisomies: a novel approach to understand DOWN syndrome and other aneuploidies G. Stamoulis, F. Santoni, P. Makrythanasis, A. Letourneau, M. Guipponi, N. Panousis, M. Garieri, P. Ribaux, E. Falconnet, C. Borel, S. E. Antonarakis O7 Role of microRNA in LCL to IPSC reprogramming S. Kumar, J. Curran, J. Blangero O8 Multiple enhancer variants disrupt gene regulatory network in Hirschsprung disease S. Chatterjee, A. Kapoor, J. Akiyama, D. Auer, C. Berrios, L. Pennacchio, A. Chakravarti O9 Metabolomic profiling for the diagnosis of neurometabolic disorders T. R. Donti, G. Cappuccio, M. Miller, P. Atwal, A. Kennedy, A. Cardon, C. Bacino, L. Emrick, J. Hertecant, F. Baumer, B. Porter, M. Bainbridge, P. Bonnen, B. Graham, R. Sutton, Q. Sun, S. Elsea O10 A novel causal methylation network approach to Alzheimer’s disease Z. Hu, P. Wang, Y. Zhu, J. Zhao, M. Xiong, David A Bennett O11 A microRNA signature identifies subtypes of triple-negative breast cancer and reveals MIR-342-3P as regulator of a lactate metabolic pathway A. Hidalgo-Miranda, S. Romero-Cordoba, S. Rodriguez-Cuevas, R. Rebollar-Vega, E. Tagliabue, M. Iorio, E. D’Ippolito, S. Baroni O12 Transcriptome analysis identifies genes, enhancer RNAs and repetitive elements that are recurrently deregulated across multiple cancer types B. Kaczkowski, Y. Tanaka, H. Kawaji, A. Sandelin, R. Andersson, M. Itoh, T. Lassmann, the FANTOM5 consortium, Y. Hayashizaki, P. Carninci, A. R. R. Forrest O13 Elevated mutation and widespread loss of constraint at regulatory and architectural binding sites across 11 tumour types C. A. Semple O14 Exome sequencing provides evidence of pathogenicity for genes implicated in colorectal cancer E. A. Rosenthal, B. Shirts, L. Amendola, C. Gallego, M. Horike-Pyne, A. Burt, P. Robertson, P. Beyers, C. Nefcy, D. Veenstra, F. Hisama, R. Bennett, M. Dorschner, D. Nickerson, J. Smith, K. Patterson, D. Crosslin, R. Nassir, N. Zubair, T. Harrison, U. Peters, G. Jarvik, NHLBI GO Exome Sequencing Project O15 The tandem duplicator phenotype as a distinct genomic configuration in cancer F. Menghi, K. Inaki, X. Woo, P. Kumar, K. Grzeda, A. Malhotra, H. Kim, D. Ucar, P. Shreckengast, K. Karuturi, J. Keck, J. Chuang, E. T. Liu O16 Modeling genetic interactions associated with molecular subtypes of breast cancer B. Ji, A. Tyler, G. Ananda, G. Carter O17 Recurrent somatic mutation in the MYC associated factor X in brain tumors H. Nikbakht, M. Montagne, M. Zeinieh, A. Harutyunyan, M. Mcconechy, N. Jabado, P. Lavigne, J. Majewski O18 Predictive biomarkers to metastatic pancreatic cancer treatment J. B. Goldstein, M. Overman, G. Varadhachary, R. Shroff, R. Wolff, M. Javle, A. Futreal, D. Fogelman O19 DDIT4 gene expression as a prognostic marker in several malignant tumors L. Bravo, W. Fajardo, H. Gomez, C. Castaneda, C. Rolfo, J. A. Pinto O20 Spatial organization of the genome and genomic alterations in human cancers K. C. Akdemir, L. Chin, A. Futreal, ICGC PCAWG Structural Alterations Group O21 Landscape of targeted therapies in solid tumors S. Patterson, C. Statz, S. Mockus O22 Genomic analysis reveals novel drivers and progression pathways in skin basal cell carcinoma S. N. Nikolaev, X. I. Bonilla, L. Parmentier, B. King, F. Bezrukov, G. Kaya, V. Zoete, V. Seplyarskiy, H. Sharpe, T. McKee, A. Letourneau, P. Ribaux, K. Popadin, N. Basset-Seguin, R. Ben Chaabene, F. Santoni, M. Andrianova, M. Guipponi, M. Garieri, C. Verdan, K. Grosdemange, O. Sumara, M. Eilers, I. Aifantis, O. Michielin, F. de Sauvage, S. Antonarakis O23 Identification of differential biomarkers of hepatocellular carcinoma and cholangiocarcinoma via transcriptome microarray meta-analysis S. Likhitrattanapisal O24 Clinical validity and actionability of multigene tests for hereditary cancers in a large multi-center study S. Lincoln, A. Kurian, A. Desmond, S. Yang, Y. Kobayashi, J. Ford, L. Ellisen O25 Correlation with tumor ploidy status is essential for correct determination of genome-wide copy number changes by SNP array T. L. Peters, K. R. Alvarez, E. F. Hollingsworth, D. H. Lopez-Terrada O26 Nanochannel based next-generation mapping for interrogation of clinically relevant structural variation A. Hastie, Z. Dzakula, A. W. Pang, E. T. Lam, T. Anantharaman, M. Saghbini, H. Cao, BioNano Genomics O27 Mutation spectrum in a pulmonary arterial hypertension (PAH) cohort and identification of associated truncating mutations in TBX4 C. Gonzaga-Jauregui, L. Ma, A. King, E. Berman Rosenzweig, U. Krishnan, J. G. Reid, J. D. Overton, F. Dewey, W. K. Chung O28 NORTH CAROLINA macular dystrophy (MCDR1): mutations found affecting PRDM13 K. Small, A. DeLuca, F. Cremers, R. A. Lewis, V. Puech, B. Bakall, R. Silva-Garcia, K. Rohrschneider, M. Leys, F. S. Shaya, E. Stone O29 PhenoDB and genematcher, solving unsolved whole exome sequencing data N. L. Sobreira, F. Schiettecatte, H. Ling, E. Pugh, D. Witmer, K. Hetrick, P. Zhang, K. Doheny, D. Valle, A. Hamosh O30 Baylor-Johns Hopkins Center for Mendelian genomics: a four year review S. N. Jhangiani, Z. Coban Akdemir, M. N. Bainbridge, W. Charng, W. Wiszniewski, T. Gambin, E. Karaca, Y. Bayram, M. K. Eldomery, J. Posey, H. Doddapaneni, J. Hu, V. R. Sutton, D. M. Muzny, E. A. Boerwinkle, D. Valle, J. R. Lupski, R. A. Gibbs O31 Using read overlap assembly to accurately identify structural genetic differences in an ashkenazi jewish trio S. Shekar, W. Salerno, A. English, A. Mangubat, J. Bruestle O32 Legal interoperability: a sine qua non for international data sharing A. Thorogood, B. M. Knoppers, Global Alliance for Genomics and Health - Regulatory and Ethics Working Group O33 High throughput screening platform of competent sineups: that can enhance translation activities of therapeutic target H. Takahashi, K. R. Nitta, A. Kozhuharova, A. M. Suzuki, H. Sharma, D. Cotella, C. Santoro, S. Zucchelli, S. Gustincich, P. Carninci O34 The undiagnosed diseases network international (UDNI): clinical and laboratory research to meet patient needs J. J. Mulvihill, G. Baynam, W. Gahl, S. C. Groft, K. Kosaki, P. Lasko, B. Melegh, D. Taruscio O36 Performance of computational algorithms in pathogenicity predictions for activating variants in oncogenes versus loss of function mutations in tumor suppressor genes R. Ghosh, S. Plon O37 Identification and electronic health record incorporation of clinically actionable pharmacogenomic variants using prospective targeted sequencing S. Scherer, X. Qin, R. Sanghvi, K. Walker, T. Chiang, D. Muzny, L. Wang, J. Black, E. Boerwinkle, R. Weinshilboum, R. Gibbs O38 Melanoma reprogramming state correlates with response to CTLA-4 blockade in metastatic melanoma T. Karpinets, T. Calderone, K. Wani, X. Yu, C. Creasy, C. Haymaker, M. Forget, V. Nanda, J. Roszik, J. Wargo, L. Haydu, X. Song, A. Lazar, J. Gershenwald, M. Davies, C. Bernatchez, J. Zhang, A. Futreal, S. Woodman O39 Data-driven refinement of complex disease classification from integration of heterogeneous functional genomics data in GeneWeaver E. J. Chesler, T. Reynolds, J. A. Bubier, C. Phillips, M. A. Langston, E. J. Baker O40 A general statistic framework for genome-based disease risk prediction M. Xiong, L. Ma, N. Lin, C. Amos O41 Integrative large-scale causal network analysis of imaging and genomic data and its application in schizophrenia studies N. Lin, P. Wang, Y. Zhu, J. Zhao, V. Calhoun, M. Xiong O42 Big data and NGS data analysis: the cloud to the rescue O. Dobretsberger, M. Egger, F. Leimgruber O43 Cpipe: a convergent clinical exome pipeline specialised for targeted sequencing S. Sadedin, A. Oshlack, Melbourne Genomics Health Alliance O44 A Bayesian classification of biomedical images using feature extraction from deep neural networks implemented on lung cancer data V. A. A. Antonio, N. Ono, Clark Kendrick C. Go O45 MAV-SEQ: an interactive platform for the Management, Analysis, and Visualization of sequence data Z. Ahmed, M. Bolisetty, S. Zeeshan, E. Anguiano, D. Ucar O47 Allele specific enhancer in EPAS1 intronic regions may contribute to high altitude adaptation of Tibetans C. Zeng, J. Shao O48 Nanochannel based next-generation mapping for structural variation detection and comparison in trios and populations H. Cao, A. Hastie, A. W. Pang, E. T. Lam, T. Liang, K. Pham, M. Saghbini, Z. Dzakula O49 Archaic introgression in indigenous populations of Malaysia revealed by whole genome sequencing Y. Chee-Wei, L. Dongsheng, W. Lai-Ping, D. Lian, R. O. Twee Hee, Y. Yunus, F. Aghakhanian, S. S. Mokhtar, C. V. Lok-Yung, J. Bhak, M. Phipps, X. Shuhua, T. Yik-Ying, V. Kumar, H. Boon-Peng O50 Breast and ovarian cancer prevention: is it time for population-based mutation screening of high risk genes? I. Campbell, M.-A. Young, P. James, Lifepool O53 Comprehensive coverage from low DNA input using novel NGS library preparation methods for WGS and WGBS C. Schumacher, S. Sandhu, T. Harkins, V. Makarov O54 Methods for large scale construction of robust PCR-free libraries for sequencing on Illumina HiSeqX platform H. DoddapaneniR. Glenn, Z. Momin, B. Dilrukshi, H. Chao, Q. Meng, B. Gudenkauf, R. Kshitij, J. Jayaseelan, C. Nessner, S. Lee, K. Blankenberg, L. Lewis, J. Hu, Y. Han, H. Dinh, S. Jireh, K. Walker, E. Boerwinkle, D. Muzny, R. Gibbs O55 Rapid capture methods for clinical sequencing J. Hu, K. Walker, C. Buhay, X. Liu, Q. Wang, R. Sanghvi, H. Doddapaneni, Y. Ding, N. Veeraraghavan, Y. Yang, E. Boerwinkle, A. L. Beaudet, C. M. Eng, D. M. Muzny, R. A. Gibbs O56 A diploid personal human genome model for better genomes from diverse sequence data K. C. C. Worley, Y. Liu, D. S. T. Hughes, S. C. Murali, R. A. Harris, A. C. English, X. Qin, O. A. Hampton, P. Larsen, C. Beck, Y. Han, M. Wang, H. Doddapaneni, C. L. Kovar, W. J. Salerno, A. Yoder, S. Richards, J. Rogers, J. R. Lupski, D. M. Muzny, R. A. Gibbs O57 Development of PacBio long range capture for detection of pathogenic structural variants Q. Meng, M. Bainbridge, M. Wang, H. Doddapaneni, Y. Han, D. Muzny, R. Gibbs O58 Rhesus macaques exhibit more non-synonymous variation but greater impact of purifying selection than humans R. A. Harris, M. Raveenedran, C. Xue, M. Dahdouli, L. Cox, G. Fan, B. Ferguson, J. Hovarth, Z. Johnson, S. Kanthaswamy, M. Kubisch, M. Platt, D. Smith, E. Vallender, R. Wiseman, X. Liu, J. Below, D. Muzny, R. Gibbs, F. Yu, J. Rogers O59 Assessing RNA structure disruption induced by single-nucleotide variation J. Lin, Y. Zhang, Z. Ouyang P1 A meta-analysis of genome-wide association studies of mitochondrial dna copy number A. Moore, Z. Wang, J. Hofmann, M. Purdue, R. Stolzenberg-Solomon, S. Weinstein, D. Albanes, C.-S. Liu, W.-L. Cheng, T.-T. Lin, Q. Lan, N. Rothman, S. Berndt P2 Missense polymorphic genetic combinations underlying down syndrome susceptibility E. S. Chen P4 The evaluation of alteration of ELAM-1 expression in the endometriosis patients H. Bahrami, A. Khoshzaban, S. Heidari Keshal P5 Obesity and the incidence of apolipoprotein E polymorphisms in an assorted population from Saudi Arabia population K. K. R. Alharbi P6 Genome-associated personalized antithrombotical therapy for patients with high risk of thrombosis and bleeding M. Zhalbinova, A. Akilzhanova, S. Rakhimova, M. Bekbosynova, S. Myrzakhmetova P7 Frequency of Xmn1 polymorphism among sickle cell carrier cases in UAE population M. Matar P8 Differentiating inflammatory bowel diseases by using genomic data: dimension of the problem and network organization N. Mili, R. Molinari, Y. Ma, S. Guerrier P9 Vulnerability of genetic variants to the risk of autism among Saudi children N. Elhawary, M. Tayeb, N. Bogari, N. Qotb P10 Chromatin profiles from ex vivo purified dopaminergic neurons establish a promising model to support studies of neurological function and dysfunction S. A. McClymont, P. W. Hook, L. A. Goff, A. McCallion P11 Utilization of a sensitized chemical mutagenesis screen to identify genetic modifiers of retinal dysplasia in homozygous Nr2e3^rd7 mice Y. Kong, J. R. Charette, W. L. Hicks, J. K. Naggert, L. Zhao, P. M. Nishina P12 Ion torrent next generation sequencing of recessive polycystic kidney disease in Saudi patients B. M. Edrees, M. Athar, F. A. Al-Allaf, M. M. Taher, W. Khan, A. Bouazzaoui, N. A. Harbi, R. Safar, H. Al-Edressi, A. Anazi, N. Altayeb, M. A. Ahmed, K. Alansary, Z. Abduljaleel P13 Digital expression profiling of Purkinje neurons and dendrites in different subcellular compartments A. Kratz, P. Beguin, S. Poulain, M. Kaneko, C. Takahiko, A. Matsunaga, S. Kato, A. M. Suzuki, N. Bertin, T. Lassmann, R. Vigot, P. Carninci, C. Plessy, T. Launey P14 The evolution of imperfection and imperfection of evolution: the functional and functionless fractions of the human genome D. Graur P16 Species-independent identification of known and novel recurrent genomic entities in multiple cancer patients J. Friis-Nielsen, J. M. Izarzugaza, S. Brunak P18 Discovery of active gene modules which are densely conserved across multiple cancer types reveal their prognostic power and mutually exclusive mutation patterns B. S. Soibam P19 Whole exome sequencing of dysplastic leukoplakia tissue indicates sequential accumulation of somatic mutations from oral precancer to cancer D. Das, N. Biswas, S. Das, S. Sarkar, A. Maitra, C. Panda, P. Majumder P21 Epigenetic mechanisms of carcinogensis by hereditary breast cancer genes J. J. Gruber, N. Jaeger, M. Snyder P22 RNA direct: a novel RNA enrichment strategy applied to transcripts associated with solid tumors K. Patel, S. Bowman, T. Davis, D. Kraushaar, A. Emerman, S. Russello, N. Henig, C. Hendrickson P23 RNA sequencing identifies gene mutations for neuroblastoma K. Zhang P24 Participation of SFRP1 in the modulation of TMPRSS2-ERG fusion gene in prostate cancer cell lines M. Rodriguez-Dorantes, C. D. Cruz-Hernandez, C. D. P. Garcia-Tobilla, S. Solorzano-Rosales P25 Targeted Methylation Sequencing of Prostate Cancer N. Jäger, J. Chen, R. Haile, M. Hitchins, J. D. Brooks, M. Snyder P26 Mutant TPMT alleles in children with acute lymphoblastic leukemia from México City and Yucatán, Mexico S. Jiménez-Morales, M. Ramírez, J. Nuñez, V. Bekker, Y. Leal, E. Jiménez, A. Medina, A. Hidalgo, J. Mejía P28 Genetic modifiers of Alström syndrome J. Naggert, G. B. Collin, K. DeMauro, R. Hanusek, P. M. Nishina P31 Association of genomic variants with the occurrence of angiotensin-converting-enzyme inhibitor (ACEI)-induced coughing among Filipinos E. M. Cutiongco De La Paz, R. Sy, J. Nevado, P. Reganit, L. Santos, J. D. Magno, F. E. Punzalan , D. Ona , E. Llanes, R. L. Santos-Cortes , R. Tiongco, J. Aherrera, L. Abrahan, P. Pagauitan-Alan; Philippine Cardiogenomics Study Group P32 The use of “humanized” mouse models to validate disease association of a de novo GARS variant and to test a novel gene therapy strategy for Charcot-Marie-Tooth disease type 2D K. H. Morelli, J. S. Domire, N. Pyne, S. Harper, R. Burgess P34 Molecular regulation of chondrogenic human induced pluripotent stem cells M. A. Gari, A. Dallol, H. Alsehli, A. Gari, M. Gari, A. Abuzenadah P35 Molecular profiling of hematologic malignancies: implementation of a variant assessment algorithm for next generation sequencing data analysis and clinical reporting M. Thomas, M. Sukhai, S. Garg, M. Misyura, T. Zhang, A. Schuh, T. Stockley, S. Kamel-Reid P36 Accessing genomic evidence for clinical variants at NCBI S. Sherry, C. Xiao, D. Slotta, K. Rodarmer, M. Feolo, M. Kimelman, G. Godynskiy, C. O’Sullivan, E. Yaschenko P37 NGS-SWIFT: a cloud-based variant analysis framework using control-accessed sequencing data from DBGAP/SRA C. Xiao, E. Yaschenko, S. Sherry P38 Computational assessment of drug induced hepatotoxicity through gene expression profiling C. Rangel-Escareño, H. Rueda-Zarate P40 Flowr: robust and efficient pipelines using a simple language-agnostic approach;ultraseq; fast modular pipeline for somatic variation calling using flowr S. Seth, S. Amin, X. Song, X. Mao, H. Sun, R. G. Verhaak, A. Futreal, J. Zhang P41 Applying “Big data” technologies to the rapid analysis of heterogenous large cohort data S. J. Whiite, T. Chiang, A. English, J. Farek, Z. Kahn, W. Salerno, N. Veeraraghavan, E. Boerwinkle, R. Gibbs P42 FANTOM5 web resource for the large-scale genome-wide transcription start site activity profiles of wide-range of mammalian cells T. Kasukawa, M. Lizio, J. Harshbarger, S. Hisashi, J. Severin, A. Imad, S. Sahin, T. C. Freeman, K. Baillie, A. Sandelin, P. Carninci, A. R. R. Forrest, H. Kawaji, The FANTOM Consortium P43 Rapid and scalable typing of structural variants for disease cohorts W. Salerno, A. English, S. N. Shekar, A. Mangubat, J. Bruestle, E. Boerwinkle, R. A. Gibbs P44 Polymorphism of glutathione S-transferases and sulphotransferases genes in an Arab population A. H. Salem, M. Ali, A. Ibrahim, M. Ibrahim P46 Genetic divergence of CYP3A5*3 pharmacogenomic marker for native and admixed Mexican populations J. C. Fernandez-Lopez, V. Bonifaz-Peña, C. Rangel-Escareño, A. Hidalgo-Miranda, A. V. Contreras P47 Whole exome sequence meta-analysis of 13 white blood cell, red blood cell, and platelet traits L. Polfus, CHARGE and NHLBI Exome Sequence Project Working Groups P48 Association of adipoq gene with type 2 diabetes and related phenotypes in african american men and women: The jackson heart study S. Davis, R. Xu, S. Gebeab, P Riestra, A Gaye, R. Khan, J. Wilson, A. Bidulescu P49 Common variants in casr gene are associated with serum calcium levels in koreans S. H. Jung, N. Vinayagamoorthy, S. H. Yim, Y. J. Chung P50 Inference of multiple-wave population admixture by modeling decay of linkage disequilibrium with multiple exponential functions Y. Zhou, S. Xu P51 A Bayesian framework for generalized linear mixed models in genome-wide association studies X. Wang, V. Philip, G. Carter P52 Targeted sequencing approach for the identification of the genetic causes of hereditary hearing impairment A. A. Abuzenadah, M. Gari, R. Turki, A. Dallol P53 Identification of enhancer sequences by ATAC-seq open chromatin profiling A. Uyar, A. Kaygun, S. Zaman, E. Marquez, J. George, D. Ucar P54 Direct enrichment for the rapid preparation of targeted NGS libraries C. L. Hendrickson, A. Emerman, D. Kraushaar, S. Bowman, N. Henig, T. Davis, S. Russello, K. Patel P56 Performance of the Agilent D5000 and High Sensitivity D5000 ScreenTape assays for the Agilent 4200 Tapestation System R. Nitsche, L. Prieto-Lafuente P57 ClinVar: a multi-source archive for variant interpretation M. Landrum, J. Lee, W. Rubinstein, D. Maglott P59 Association of functional variants and protein physical interactions of human MUTY homolog linked with familial adenomatous polyposis and colorectal cancer syndrome Z. Abduljaleel, W. Khan, F. A. Al-Allaf, M. Athar , M. M. Taher, N. Shahzad P60 Modification of the microbiom constitution in the gut using chicken IgY antibodies resulted in a reduction of acute graft-versus-host disease after experimental bone marrow transplantation A. Bouazzaoui, E. Huber, A. Dan, F. A. Al-Allaf, W. Herr, G. Sprotte, J. Köstler, A. Hiergeist, A. Gessner, R. Andreesen, E. Holler P61 Compound heterozygous mutation in the LDLR gene in Saudi patients suffering severe hypercholesterolemia F. Al-Allaf, A. Alashwal, Z. Abduljaleel, M. Taher, A. Bouazzaoui, H. Abalkhail, A. Al-Allaf, R. Bamardadh, M. Athar

Relationship of prediagnostic body mass index with survival after colorectal cancer: Stage-specific associations

Higher body mass index (BMI) is a well-established risk factor for colorectal cancer (CRC), but is inconsistently associated with CRC survival. In 6 prospective studies participating in the Genetics and Epidemiology of Colorectal Cancer Consortium (GECCO), 2,249 non-Hispanic white CRC cases were followed for a median 4.5 years after diagnosis, during which 777 died, 554 from CRC-related causes. Associations between prediagnosis BMI and survival (overall and CRC-specific) were evaluated using Cox regression models adjusted for age at diagnosis, sex, study and smoking status (current/former/never). The association between BMI category and CRC survival varied by cancer stage at diagnosis (I-IV) for both all-cause (p-interaction = 0.03) and CRC-specific mortality (p-interaction = 0.04). Compared to normal BMI (18.5-24.9 kg/m(2) ), overweight (BMI 25.0-29.9) was associated with increased mortality among those with Stage I disease, and decreased mortality among those with Stages II-IV disease. Similarly, obesity (BMI ≥30) was associated with increased mortality among those with Stages I-II disease, and decreased mortality among those with Stages III-IV disease. These results suggest the relationship between BMI and survival after CRC diagnosis differs by stage at diagnosis, and may emphasize the importance of adequate metabolic reserves for colorectal cancer survival in patients with late-stage disease.

Testing the role of predicted gene knockouts in human anthropometric trait variation

Although the role of complete gene inactivation by two loss-of-function mutations inherited in trans is well-established in recessive Mendelian diseases, we have not yet explored how such gene knockouts (KOs) could influence complex human phenotypes. Here, we developed a statistical framework to test the association between gene KOs and quantitative human traits. Our method is flexible, publicly available, and compatible with common genotype format files (e.g. PLINK and vcf). We characterized gene KOs in 4498 participants from the NHLBI Exome Sequence Project (ESP) sequenced at high coverage (>100×), 1976 French Canadians from the Montreal Heart Institute Biobank sequenced at low coverage (5.7×), and >100 000 participants from the Genetic Investigation of ANthropometric Traits (GIANT) Consortium genotyped on an exome array. We tested associations between gene KOs and three anthropometric traits: body mass index (BMI), height and BMI-adjusted waist-to-hip ratio (WHR). Despite our large sample size and multiple datasets available, we could not detect robust associations between specific gene KOs and quantitative anthropometric traits. Our results highlight several limitations and challenges for future gene KO studies in humans, in particular when there is no prior knowledge on the phenotypes that might be affected by the tested gene KOs. They also suggest that gene KOs identified with current DNA sequencing methodologies probably do not strongly influence normal variation in BMI, height, and WHR in the general human population.

CYP24A1 variant modifies the association between use of oestrogen plus progestogen therapy and colorectal cancer risk

BACKGROUND

Menopausal hormone therapy (MHT) use has been consistently associated with a decreased risk of colorectal cancer (CRC) in women. Our aim was to use a genome-wide gene-environment interaction analysis to identify genetic modifiers of CRC risk associated with use of MHT.

METHODS

We included 10 835 postmenopausal women (5419 cases and 5416 controls) from 10 studies. We evaluated use of any MHT, oestrogen-only (E-only) and combined oestrogen-progestogen (E+P) hormone preparations. To test for multiplicative interactions, we applied the empirical Bayes (EB) test as well as the Wald test in conventional case-control logistic regression as primary tests. The Cocktail test was used as secondary test.

RESULTS

The EB test identified a significant interaction between rs964293 at 20q13.2/CYP24A1 and E+P (interaction OR (95% CIs)=0.61 (0.52-0.72), P=4.8 × 10(-9)). The secondary analysis also identified this interaction (Cocktail test OR=0.64 (0.52-0.78), P=1.2 × 10(-5) (alpha threshold=3.1 × 10(-4)). The ORs for association between E+P and CRC risk by rs964293 genotype were as follows: C/C, 0.96 (0.61-1.50); A/C, 0.61 (0.39-0.95) and A/A, 0.40 (0.22-0.73), respectively.

CONCLUSIONS

Our results indicate that rs964293 modifies the association between E+P and CRC risk. The variant is located near CYP24A1, which encodes an enzyme involved in vitamin D metabolism. This novel finding offers additional insight into downstream pathways of CRC etiopathogenesis.

Analysis of Heritability and Shared Heritability Based on Genome-Wide Association Studies for Thirteen Cancer Types

BACKGROUND

Studies of related individuals have consistently demonstrated notable familial aggregation of cancer. We aim to estimate the heritability and genetic correlation attributable to the additive effects of common single-nucleotide polymorphisms (SNPs) for cancer at 13 anatomical sites.

METHODS

Between 2007 and 2014, the US National Cancer Institute has generated data from genome-wide association studies (GWAS) for 49 492 cancer case patients and 34 131 control patients. We apply novel mixed model methodology (GCTA) to this GWAS data to estimate the heritability of individual cancers, as well as the proportion of heritability attributable to cigarette smoking in smoking-related cancers, and the genetic correlation between pairs of cancers.

RESULTS

GWAS heritability was statistically significant at nearly all sites, with the estimates of array-based heritability, hl (2), on the liability threshold (LT) scale ranging from 0.05 to 0.38. Estimating the combined heritability of multiple smoking characteristics, we calculate that at least 24% (95% confidence interval [CI] = 14% to 37%) and 7% (95% CI = 4% to 11%) of the heritability for lung and bladder cancer, respectively, can be attributed to genetic determinants of smoking. Most pairs of cancers studied did not show evidence of strong genetic correlation. We found only four pairs of cancers with marginally statistically significant correlations, specifically kidney and testes (ρ = 0.73, SE = 0.28), diffuse large B-cell lymphoma (DLBCL) and pediatric osteosarcoma (ρ = 0.53, SE = 0.21), DLBCL and chronic lymphocytic leukemia (CLL) (ρ = 0.51, SE =0.18), and bladder and lung (ρ = 0.35, SE = 0.14). Correlation analysis also indicates that the genetic architecture of lung cancer differs between a smoking population of European ancestry and a nonsmoking Asian population, allowing for the possibility that the genetic etiology for the same disease can vary by population and environmental exposures.

CONCLUSION

Our results provide important insights into the genetic architecture of cancers and suggest new avenues for investigation.

Common genetic variation and survival after colorectal cancer diagnosis: a genome-wide analysis

Genome-wide association studies have identified several germline single nucleotide polymorphisms (SNPs) significantly associated with colorectal cancer (CRC) incidence. Common germline genetic variation may also be related to CRC survival. We used a discovery-based approach to identify SNPs related to survival outcomes after CRC diagnosis. Genome-wide genotyping arrays were conducted for 3494 individuals with invasive CRC enrolled in six prospective cohort studies (median study-specific follow-up = 4.2-8.1 years). In pooled analyses, we used Cox regression to assess SNP-specific associations with CRC-specific and overall survival, with additional analyses stratified by stage at diagnosis. Top findings were followed-up in independent studies. A P value threshold of P < 5×10(-8) in analyses combining discovery and follow-up studies was required for genome-wide significance. Among individuals with distant-metastatic CRC, several SNPs at 6p12.1, nearest the ELOVL5 gene, were statistically significantly associated with poorer survival, with the strongest associations noted for rs209489 [hazard ratio (HR) = 1.8, P = 7.6×10(-10) and HR = 1.8, P = 3.7×10(-9) for CRC-specific and overall survival, respectively). No SNPs were statistically significantly associated with survival among all cases combined or in cases without distant-metastases. SNPs in 6p12.1/ELOVL5 were associated with survival outcomes in individuals with distant-metastatic CRC, and merit further follow-up for functional significance. Findings from this genome-wide association study highlight the potential importance of genetic variation in CRC prognosis and provide clues to genomic regions of potential interest.