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Biomedical subjects

Eivind Hovig

Publications and source records attributed to Eivind Hovig.

6 recordsLinked to original sources
PubMed2026

Family genetic designs in MoBa provide insights into health and functioning.

Genome-wide association studies using large, population-based samples of unrelated individuals have discovered thousands of genetic associations with health and disease1. These studies can help explain genetic and environmental risks. However, increasing evidence suggests that population-based estimates, while precise, can also reflect confounding that affects their use and interpretation. This confounding can be overcome using data from genotyped family members, such as nuclear mother-father-child trios2,3. However, samples of genotyped families are rare4-11. Here we illustrate some of the advantages of familial data using the Norwegian Mother, Father and Child Cohort Study (MoBa), a population-based cohort of parents and offspring with extensive genotype data (n ≈ 230,000) (ref. 3), along with broad and longitudinal phenotyping of health and functioning. We provide an overview of MoBa and describe the quality control of genotype data tailored to this extensively related sample. We then use trio data to illustrate how family-based genomic designs can identify distinct direct and indirect sources of genetic influence and structural confounding. As examples, we analyse children's height, educational achievement, depressive symptoms and sleep duration. These demonstrations highlight MoBa as a broadly valuable resource for advancing understanding of health and functioning across the lifecourse and generations.

Journal Article↗
PubMed2003

Effects of mRNA amplification on gene expression ratios in cDNA experiments estimated by analysis of variance.

BACKGROUND: A limiting factor of cDNA microarray technology is the need for a substantial amount of RNA per labeling reaction. Thus, 20-200 micro-grams total RNA or 0.5-2 micro-grams poly (A) RNA is typically required for monitoring gene expression. In addition, gene expression profiles from large, heterogeneous cell populations provide complex patterns from which biological data for the target cells may be difficult to extract. In this study, we chose to investigate a widely used mRNA amplification protocol that allows gene expression studies to be performed on samples with limited starting material. We present a quantitative study of the variation and noise present in our data set obtained from experiments with either amplified or non-amplified material. RESULTS: Using analysis of variance (ANOVA) and multiple hypothesis testing, we estimated the impact of amplification on the preservation of gene expression ratios. Both methods showed that the gene expression ratios were not completely preserved between amplified and non-amplified material. We also compared the expression ratios between the two cell lines for the amplified material with expression ratios between the two cell lines for the non-amplified material for each gene. With the aid of multiple t-testing with a false discovery rate of 5%, we found that 10% of the genes investigated showed significantly different expression ratios. CONCLUSION: Although the ratios were not fully preserved, amplification may prove to be extremely useful with respect to characterizing low expressing genes.

Analysis of Variance↗
PubMed2002

Analysis of repeatability in spotted cDNA microarrays.

We report a strategy for analysis of data quality in cDNA microarrays based on the repeatability of repeatedly spotted clones. We describe how repeatability can be used to control data quality by developing adaptive filtering criteria for microarray data containing clones spotted in multiple spots. We have applied the method on five publicly available cDNA microarray data sets and one previously unpublished data set from our own laboratory. The results demonstrate the feasibility of the approach as a foundation for data filtering, and indicate a high degree of variation in data quality, both across the data sets and between arrays within data sets.

Oligonucleotide Array Sequence Analysis↗
PubMed2002

Differential display analysis of breast carcinoma cells enriched by immunomagnetic target cell selection: gene expression profiles in bone marrow target cells.

The red bone marrow (BM) is an important indicator organ of hematogenous micrometastatic spread of carcinomas. Characterization of biological properties specific for BM micrometastatic cells, however, is technically challenging due to the limited number of target cells usually available for the purpose. This report provides referrals to qualitative gene expression profiling of BM micrometastatic cells enriched by immunomagnetic selection. First, an experimental strategy was used to study regulatory mechanisms involved when BM micrometastatic cells colonize distant organs. The MA-11 cells, originating from BM micrometastases in a breast cancer patient clinically devoid of overt metastatic disease, were injected into immunodeficient rats. Metastatic MA-11 cells were subsequently immunoselected from the resulting in vivo lesions. The selected cell populations were compared to the injected cells by differential display analysis, and several genes possibly involved in tumor cell invasion and proliferation were confirmed as differentially expressed among the various MA-11 cell populations. A direct approach to qualitative gene expression profiling of BM micrometastatic cells was also explored. Carcinoma cells were immunoselected from BM and axillary lymph nodes obtained from breast cancer patients, and the isolated cell populations were compared by differential display analysis. Two candidate genes, identified as factors involved in cellular growth control, appeared as differentially expressed by the target cells from BM. Our study provides detailed information on how to combine an immunomagnetic selection procedure and differential display analysis to reveal gene expression profiles that may characterize BM micrometastatic cells.

Animals↗
PubMed2002

MArray: analysing single, replicated or reversed microarray experiments.

UNLABELLED: MArray is a Matlab toolbox with a graphical user interface that allows the user to analyse single or paired microarray datasets by direct input of the raw data output file from image analysis packages, such as QuantArray or GenePiX. The application provides simple procedures to manually evaluate the quality of each measurement, multiple approaches to both ratio normalization (simple normalization, intensity dependent normalization) and evaluation of the reproducibility of paired experiments (using the techniques 'simple statistical method' and 'quality control ellipse' and 'significance analysis of microarrays'). Specifically, interactive spot evaluation functions are available in MArray and an online gene information database (NCBI UniGene) is linked. The application may provide a valuable aid in selecting and optimizing experimental procedures, as well as serving as an analytical tool for two-state biological comparisons, such as a study of single-dose activation. It is entirely platform independent, and only requires Matlab installed. AVAILABILITY: http://matrise.uio.no/marray/marray.html

Computer Graphics↗