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Pancreatic Neoplasms: HELP
Articles by Agnes Viale
Based on 2 articles published since 2010
(Why 2 articles?)
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Between 2010 and 2020, Agnes Viale wrote the following 2 articles about Pancreatic Neoplasms.
 
+ Citations + Abstracts
1 Article Genome-wide analysis of the role of copy-number variation in pancreatic cancer risk. 2014

Willis, Jason A / Mukherjee, Semanti / Orlow, Irene / Viale, Agnes / Offit, Kenneth / Kurtz, Robert C / Olson, Sara H / Klein, Robert J. ·Department of Medicine, Memorial Sloan-Kettering Cancer Center New York, NY, USA ; Program in Cancer Biology and Genetics, Memorial Sloan-Kettering Cancer Center New York, NY, USA. · Program in Cancer Biology and Genetics, Memorial Sloan-Kettering Cancer Center New York, NY, USA. · Department of Epidemiology and Biostatistics, Memorial Sloan-Kettering Cancer Center New York, NY, USA. · Genomics Core Laboratory, Memorial Sloan-Kettering Cancer Center New York, NY, USA. · Department of Medicine, Memorial Sloan-Kettering Cancer Center New York, NY, USA. ·Front Genet · Pubmed #24592275.

ABSTRACT: Although family history is a risk factor for pancreatic adenocarcinoma, much of the genetic etiology of this disease remains unknown. While genome-wide association studies have identified some common single nucleotide polymorphisms (SNPs) associated with pancreatic cancer risk, these SNPs do not explain all the heritability of this disease. We hypothesized that copy number variation (CNVs) in the genome may play a role in genetic predisposition to pancreatic adenocarcinoma. Here, we report a genome-wide analysis of CNVs in a small hospital-based, European ancestry cohort of pancreatic cancer cases and controls. Germline CNV discovery was performed using the Illumina Human CNV370 platform in 223 pancreatic cancer cases (both sporadic and familial) and 169 controls. Following stringent quality control, we asked if global CNV burden was a risk factor for pancreatic cancer. Finally, we performed in silico CNV genotyping and association testing to discover novel CNV risk loci. When we examined the global CNV burden, we found no strong evidence that CNV burden plays a role in pancreatic cancer risk either overall or specifically in individuals with a family history of the disease. Similarly, we saw no significant evidence that any particular CNV is associated with pancreatic cancer risk. Taken together, these data suggest that CNVs do not contribute substantially to the genetic etiology of pancreatic cancer, though the results are tempered by small sample size and large experimental variability inherent in array-based CNV studies.

2 Article Including additional controls from public databases improves the power of a genome-wide association study. 2011

Mukherjee, Semanti / Simon, Jennifer / Bayuga, Sharon / Ludwig, Emmy / Yoo, Sarah / Orlow, Irene / Viale, Agnes / Offit, Kenneth / Kurtz, Robert C / Olson, Sara H / Klein, Robert J. ·Gerstner Sloan-Kettering Graduate School of Biomedical Sciences, Memorial Sloan-Kettering Cancer Center, New York, USA. ·Hum Hered · Pubmed #21849791.

ABSTRACT: Though genome-wide association studies (GWAS) have identified numerous susceptibility loci for common diseases, their use is limited due to the expense of genotyping large cohorts of individuals. One potential solution is to use 'additional controls', or genotype data from control individuals deposited in public repositories. While this approach has been used by several groups, the genetically heterogeneous nature of the population of the United States makes this approach potentially problematic. We empirically investigated the utility of this approach in a US-based GWAS. In a small GWAS of pancreatic cancer in New York, we observed clear population structure differences relative to controls from the database of Genotypes and Phenotypes (dbGaP). When we conduct the GWAS using these additional controls, we find large inflation of the test statistic that is properly corrected by using eigenvectors from principal components analysis as covariates. To deal with errors introduced due to different sources, we propose simultaneously genotyping a small number of controls along with cases and then comparing this group to the additional controls. We show that removing SNPs that show differences between these control groups reduces false-positive findings. Thus, through an empirical approach, this report provides practical guidance for using additional controls from publicly available datasets.