Biomedical subjects

Hege Edvardsen

Publications and source records attributed to Hege Edvardsen.

5 recordsLinked to original sources

PubMed2006

ATM variants and cancer risk in breast cancer patients from Southern Finland.

BACKGROUND: Individuals heterozygous for germline ATM mutations have been reported to have an increased risk for breast cancer but the role for ATM genetic variants for breast cancer risk has remained unclear. Recently, a common ATM variant, ATMivs38 -8T>C in cis with the ATMex39 5557G>A (D1853N) variant, was suggested to associate with bilateral breast cancer among familial breast cancer patients from Northern Finland. We have here evaluated the 5557G>A and ivs38-8T>C variants in an extensive case-control association analysis. We also aimed to investigate whether there are other ATM mutations or variants contributing to breast cancer risk in our population. METHODS: Two common ATM variants, 5557G>A and ivs38-8T>C, previously suggested to associate with bilateral breast cancer, were genotyped in an extensive set of 786 familial and 884 unselected breast cancer cases as well as 708 healthy controls. We also screened the entire coding region and exon-intron boundaries of the ATM gene in 47 familial breast cancer patients and constructed haplotypes of the patients. The identified variants were also evaluated for increased breast cancer risk among additional breast cancer cases and controls. RESULTS: Neither of the two common variants, 5557G>A and ivs38-8T>C, nor any haplotype containing them, was significantly associated with breast cancer risk, bilateral breast cancer or multiple primary cancers in any of the patient groups or subgoups. Three rare missense alterations and one intronic change were each found in only one patient of over 250 familial patients studied and not among controls. The fourth missense alteration studied further was found with closely similar frequencies in over 600 familial cases and controls. CONCLUSION: Altogether, our results suggest very minor effect, if any, of ATM genetic variants on familial breast cancer in Southern Finland. Our results do not support association of the 5557G>A or ivs38-8T>C variant with increased breast cancer risk or with bilateral breast cancer.

Ataxia Telangiectasia Mutated Proteins↗

PubMed2006

Genetic variation in putative regulatory loci controlling gene expression in breast cancer.

Candidate single-nucleotide polymorphisms (SNPs) were analyzed for associations to an unselected whole genome pool of tumor mRNA transcripts in 50 unrelated patients with breast cancer. SNPs were selected from 203 candidate genes of the reactive oxygen species pathway. We describe a general statistical framework for the simultaneous analysis of gene expression data and SNP genotype data measured for the same cohort, which revealed significant associations between subsets of SNPs and transcripts, shedding light on the underlying biology. We identified SNPs in EGF, IL1A, MAPK8, XPC, SOD2, and ALOX12 that are associated with the expression patterns of a significant number of transcripts, indicating the presence of regulatory SNPs in these genes. SNPs were found to act in trans in a total of 115 genes. SNPs in 43 of these 115 genes were found to act both in cis and in trans. Finally, subsets of SNPs that share significantly many common associations with a set of transcripts (biclusters) were identified. The subsets of transcripts that are significantly associated with the same set of SNPs or to a single SNP were shown to be functionally coherent in Gene Ontology and pathway analyses and coexpressed in other independent data sets, suggesting that many of the observed associations are within the same functional pathways. To our knowledge, this article is the first study to correlate SNP genotype data in the germ line with somatic gene expression data in breast tumors. It provides the statistical framework for further genotype expression correlation studies in cancer data sets.

Breast Neoplasms↗

PubMed2006

Experimental validation of data mined single nucleotide polymorphisms from several databases and consecutive dbSNP builds.

Rapid development in the annotation of human genetic variation has increased the numbers of single nucleotide polymorphisms (SNPs) in candidate genes by several orders of magnitude. The selection of both useful target SNPs for disease-gene association studies and SNPs associated with the treatment response is therefore an increasingly challenging task. We describe a workflow for selecting SNPs based on their putative function and frequency in candidate genes extracted from PubMed resources. The annotation of each SNP and its frequency in a Caucasian population was assessed in several databases. Approximately 4000 SNPs were identified from an initial 233 candidate genes. In a case study, we performed actual genotyping of 1030 of these SNPs in 213 genes and obtained 710 successfully genotyped SNPs. Using the flow-chart outlined here, only 87 SNPs were monomorphic (approximately 12%). This study reports the frequency of SNPs in a Caucasian population, selected in silico, using a candidate gene approach and validated by actually genotyping 193 individuals. The selected genotypes represent a valuable set of verified candidate SNPs for pharmacogenetic studies in Caucasian populations.

Breast Neoplasms↗

PubMed2006

Analysis of SNP-expression association matrices.

High throughput expression profiling and genotyping technologies provide the means to study the genetic determinants of population variation in gene expression variation. In this paper we present a general statistical framework for the simultaneous analysis of gene expression data and SNP genotype data measured for the same cohort. The framework consists of methods to associate transcripts with SNPs affecting their expression, algorithms to detect subsets of transcripts that share significantly many associations with a subset of SNPs, and methods to visualize the identified relations. We apply our framework to SNP-expression data collected from 50 breast cancer patients. Our results demonstrate an overabundance of transcript-SNP associations in this data, and pinpoint SNPs that are potential master regulators of transcription. We also identify several statistically significant transcript-subsets with common putative regulators that fall into well-defined functional categories.

Algorithms↗

PubMed2005

Analysis of SNP-expression association matrices.

High throughput expression profiling and genotyping technologies provide the means to study the genetic determinants of population variation in gene expression variation. In this paper we present a general statistical framework for the simultaneous analysis of gene expression data and SNP genotype data measured for the same cohort. The framework consists of methods to associate transcripts with SNPs affecting their expression, algorithms to detect subsets of transcripts that share significantly many associations with a subset of SNPs, and methods to visualize the identified relations. We apply our framework to SNP-expression data collected from 49 breast cancer patients. Our results demonstrate an overabundance of transcript-SNP associations in this data, and pinpoint SNPs that are potential master regulators of transcription. We also identify several statistically significant transcript-subsets with common putative regulators that fall into well-defined functional categories.

Algorithms↗