DNA variation in Norway

1 Rationale

To determine the genetic causes of phenotypes, it is necessary to characterise the actual genotypic variation within linkage distance of the causative genetic code. Genetic variation differs between populations. Norway has unique human biobanks relevant for many complex diseases and a new high-throughput SNP platform. This project has 3 main aims

to determine the DNA variation of the Norwegian population, to start exploiting this knowledge to understand specific diseases and other

important phenotypes, and to develop advanced tools in statistics for functional genomics.

Earlier efforts to utilise DNA variation information available from international resources for the Norwegian population indicate that less than half of identified points of variation available from international sources will in fact be present in the Norwegian population, and are often of limited value because of low allele frequencies. Thus, there is a need to provide adequate information and tools to the scientific community to facilitate the identification of the gene underlying unidentified single gene based diseases, as well as multiple genes in complex trait phenotypes, with their relative contributions. Specifically, we aim to use polymorphic markers, methods and reagents at a resolution of at least 5 cM (or finer, depending on aggregate available funding) evenly spaced along the genome, to estimate their respective allele frequencies. As the human genome contains a total of approximately 3600 cM1, this implies identifying around 750 informative markers. Optimally, these markers should be informative so that the least frequent allele is present in approximately one third of the population. This resource will represent a major step forward for most clinically relevant questions addressable in both classical and high throughput modalities. The selected strategy uses markers in expressed regions of important and immediately interesting genes, such as those predisposing for cancer, DNA repair genes, CYP and GPCR genes and other important genes. In this way, it opens for searches for modifying genes in disease, such as those relevant for perinatal outcomes, allergies, cancer penetrance and drug metabolism. The study population is representative for the whole Norwegian population. We will determine the frequencies for all 60000 mothers (of Norwegian descent in one year). A broad sample is provided as the Mother and Child cohort of the Norwegian Biobank platform. Most phenotypic traits should be available for inquiry, with minimal selection biases. To show the usefulness of our approach and start to exploit the information produced, we include subprojects in important clinical topics, where statistical association mapping of candidate genes and phenotype is performed. In particular we shall study the genetic and environmental causes of

preeclampsia asthma

breast cancer disease in the male reproductive

system.Furthermore, the success of high throughput technologies poses major statistical challenges in order to merge genotyping results and large-scale gene expression data with other clinical, phenotypic and environmental exposure data and to convert these into biological knowledge. Statistics plays a central role in the production, analysis and interpretation of these data, and a major effort in enhancing the field of statistical genomics is needed. Current important statistical methodologies include experimental design, pattern discovery,

1 Kong A, Gudbjartsson DF, Sainz J, Jonsdottir GM, Gudjonsson SA, Richardsson B, Sigurdardottir S, Barnard J, Hallbeck B, Masson G, Shlien A, Palsson ST, Frigge ML, Thorgeirsson TE, Gulcher JR, Stefansson K.: A high-resolution recombination map of the human genome. Nat Genet. 2002 Jul;31(3):241-7.

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clustering, multivariate regression, hierarchical inference, multiple testing and event history analysis. A characteristic feature is the many sources of systematic and random errors, complex dependencies between measurements, and few observations. This interdisciplinary proposal (Figure 1) also has the aim to produce innovative statistical methods for their analysis. A significant part of analytical tools and strategies within this project is by nature species independent. In the course of the project, we aim to stimulate and participate in the development of a broad scientific environment within the region, encompassing production biology (salmon, cattle, barley etc.) without a significant species barrier. This will be achieved both by active support of the SNP CIGENE platform, through collaboration and joint knowledge distribution, and by the strong links to the biobank, bioinformatics and microarray FUGE platforms, with which a similar integrated strategy will be adopted. The project will also try to connect to the HapMap2 project with respect to fine mapping efforts, both in selection of SNPs and in providing information content.

2. DNA variation in Norway

The genetic composition of the Norwegian population differs from other populations probably because of its relative isolation over centuries after the breakdown due to the Bubonic plague, some 25 generations ago. A small population with a rapid expansion generates a genetic drift, a random drift away from the initial mean genetic composition. The mathematics of such dynamics is well understood, but the implications of such a situation have often been ignored, when assuming results from other populations to be directly valid for the Norwegian one. We want to take benefit from these imbalances, as they in fact provide an increase in the precision in describing the Norwegian population. There are distributions, both in normal and in disease causing DNA variation, that make the Norwegian population suitable for identifying the genetic cause of locally frequent inherited phenotypes, including inherited diseases. We describe the distribution of normal DNA variation in a representative Norwegian population. This is a first necessary tool for genetic research focusing on distinct diseases. In order to precisely identify the correct genes for specific phenotypes, more finely mapped local chromosomal regions are needed, beyond a first order resolution. This specific mapping falls within each research line. The resulting fine mapping information will be contributed back to the common resource for future use. Thus, a gradually more populated marker map will be built.

The DNA source: The Norwegian Mother and Child Cohort (M&C), now including 25,000 women, has the purpose to aid finding causes of serious diseases, related to genetic and to environmental factors, such as medications, nutrition, infections and work exposure (www.fhi.no/tema/morogbarn). The recruitment is based on 40 hospitals in all counties in Norway. More than 90 % of Norwegian women will give birth to a child. The cohort therefore provides a very comprehensive sample of Norwegian genotypes. About 80 % of partners of recruited women also participate. There is ample DNA for genotyping, since the mothers provide blood samples both during and after pregnancy.

An association between phenotypic traits and the DNA variation is either directly causing the phenotypic trait, or in linkage disequilibrium with the cause of the phenotypic trait. A further possibility is false stratification due to sampling error. Because large blocks of DNA are passed unbroken, linkage disequilibrium is extensive inside families. Statistical methods have been developed to make use of intrafamilial associations, including affected

2 Cardon LR, Abecasis GR.: Using haplotype blocks to map human complex trait loci.Trends Genet. 2003 Mar;19(3):135-40.

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sib methods and transmission disequilibrium tests, the aim being to describe blocks (haplotypes) of DNA comprising genetic factors causing disease.

Recently, several initiatives have been proposed to characterize the human phenome, among which are The Human Phenome Project3 and the Human Genome Variation database4. The aim is to develop and exploit common descriptives and datasets for all human traits for which there is a describable phenotype, including all high throughput data resulting from genotypic variation, and the effects of the environment. This project will actively participate in these phenome projects. In the course of this project, a wide host of different phenotypes will be available for exploitation, including both disease markers and expression information, which is a fully valid phenotype. The links between expression profiling and genotypic information have so far not been extensively examined. An example is the link between BRCA gene mutations in hereditary breast cancer and expression clustering5, clearly demonstrating the potential usefulness of this approach. This is also a highly pertinent approach to study environmental factors and genotype interaction.

To achieve the proposed goal, internationally available databases with DNA variation will be used. Two main strategies are available. One is to rely mainly on SNP databases and mapping efforts, another is to utilize microsatellites, or short tandem repeat polymorphisms (STRPs). A combination of these approaches is also possible. Microsatellites do generally not reside in coding regions, but have a very high informational content, while SNPs are more frequently available for coding regions. As the national FUGE SNP platform primarily provides SNP data, this will be a main focus, not least for fine mapping.

No funding is hitherto available to the FUGE SNP platform for performing SNP typing on the Norwegian population, and to our knowledge, no other concerted effort is being made to obtain a genome-scaled allele frequency map of the Norwegian population. In order to make the project economically feasible, 60 pools with 1000 individuals each will be made. These will be examined for 1000 SNPs using an in house platform of denaturing capillary electrophoresis on a modified MegaBace sequencing instrument. The setup has a published sensitivity of less than one allele per 1000 in a pooled format, and with proven reliability in frequency determination6. A few hundred individuals will be additionally determined on both the in house and FUGE SNP platform for independent verification. As the primers (and conditions) generated through this effort will be far in excess of need, the material will be available for subproject typing. The DNA samples, available in 96 well plates, will be amplified in situ at either site, through the use of the high throughput liquid handling robotics available for this project. The selection of markers and evaluation of typing results will be the responsibility of our clinical genomics team, in association with others as needed. The present application thus integrates with the FUGE SNP platform with respect to typing technology and opens for a flexible typing strategy for subproject basic typing.

3 Freimer N, Sabatti C.: The human phenome project. Nat Genet. 2003 May;34(1):15-214 http://hgvbase.cgb.ki.se/5 Botstein D, Risch N. Discovering genotypes underlying human phenotypes: past successes for mendelian disease, future approaches for complex disease. Nat Genet. 2003 Mar;33 Suppl:228-37. Hedenfalk I, Ringner M, Ben-Dor A, Yakhini Z, Chen Y, Chebil G, Ach R, Loman N, Olsson H, Meltzer P, Borg A, Trent J. Molecular classification of familial non-BRCA1/BRCA2 breast cancer. Proc Natl Acad Sci U S A. 2003 Mar 4;100(5):2532-7.Hedenfalk IA.:Gene expression profiling of hereditary and sporadic ovarian cancers reveals unique BRCA1 and BRCA2 signatures. J Natl Cancer Inst. 2002 Jul 3;94(13):960-1.Hedenfalk I, Duggan D, Chen Y, Radmacher M, Bittner M, Simon R, Meltzer P, Gusterson B, Esteller M, Kallioniemi OP, Wilfond B, Borg A, Trent J.:Gene-expression profiles in hereditary breast cancer.N Engl J Med. 2001 Feb 22;344(8):539-48.

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For details on the technologies (both SNPs and microsatellites) used at the FUGE SNP platform, we refer to its webpage. The specific needs of our subprojects will be coordinated by the steering group of the project. The use of local typing platforms for fine mapping studies will be incorporated into the overall strategy. All downstream data handling will be coordinated with the relevant FUGE platforms, i.e. biobanks, bioinformatics, microarray and SNP. This is a prerequisite in generating data that will have lasting importance on the necessary suite of phenotypical and information handling of human biology. 3. Genetic and environmental causes of preeclampsia

Preeclampsia affects 3-5% of all pregnancies and is a major cause of foetal and maternal morbidity and mortality. The genetic mechanisms underlying susceptibility to preeclampsia remain unclear. While early genetic studies focused on a susceptibility locus in the mother7, a few studies have later shown a role also for the paternal genotype8. Several authors have studied the association of preeclampsia to candidate genes, with conflicting results9. HLA-G is a non-classical major histocompatibility complex (MHC) class 1 molecule located on chromosome 6 with tissue restricted distribution normally expressed in invasive trophoblasts. HLA-G was has been recognized as a key mediator in immune tolerance10, and there is evidence that the expression is strongly reduced in placentas from women with preeclampsia11. Expression of HLA-G in trophoblasts has been shown to protect cells from lysis by the maternal NK cells via HLA-G interaction with specific receptors on NK cells.

Trophoblast HLA-G presents a novel gene, Epstein-Barr virus-induced gene 3 (EBI3). EBI3 is expressed in Epstein-Barr Virus (EBV)-transformed B-lymphocytes, tonsil, spleen and trophoblasts. EBI3 levels have been found strongly upregulated in sera from pregnant women, expressed throughout pregnancy by syncytio- and extravillous trophoblasts. The expression gradually increases with increasing gestational age12. We have previously shown13 that pregnant women who were seronegative for antibodies against Epstein-Barr virus in early pregnancy had an increased risk of developing preeclampsia as compared to a reference group of seropositive women. However, extended analyses in the same dataset reveal an increased risk of preeclampsia in both seronegative (aOR 6.3) and IgG positive women (aOR 2.2) as compared to women with reactivated or incident infection in early pregnancy (unpublished data). The implication of this preliminary finding is that reactivation of latent EBV infection or de novo infection in early pregnancy protects against preeclampsia. Since M&C includes plasma samples from pregnancy (17th week) as well as immediately after pregnancy, we can diagnose reactivation of latent and de novo EBV infection in M&C. From the first 20 000 women who participate in M&C, we expect 500-600 cases of preeclampsia. DNA from these cases and their parents (case triads) will be SNP-genotyped together the same number of control triads. This approach allows for detection of effects from genes (SNPs, haplotypes) as well as environmental exposures, for estimation of interaction effects (gene-gene and gene-environment) and the detection of maternal effects. Of particular interest is the study of interactions between specific paternally derived alleles in the foetus and maternal genes that govern immune response. Studies have indicated possible association between pre-eclampsia and variants of genetic thrombophilia (factor V Leiden G506A mutation, prothrombin G20210A, MTHFR C677T). Genes related to regulation of blood pressure (angiotensinogen M235T, angiotensin II reseptor 1 and rennin) will also be studied. Statistical methods for association studies based on case-triads will incorporate estimation of haplotype effects based on SNP genotyping of these genes and possible between gene interactions.

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4. Genetic and environmental causes of asthma

A Norwegian twin study has shown a high heritability (0.8) of asthma14. Two approaches have been used to find asthma genes, candidate gene allelic association studies and posi-tional cloning after identification of chromosomal regions of interest. A series of genes and regions have been suggested, such as the recent demonstration of the association to the PHF11 gene at chromosome 1315 but there is still uncertainty as to their relative contribu-tion to the aetiology of asthma. We have access to two important cohorts in asthma re-search: M&C and the Oslo birth cohort that has been followed since 1992. Environmental exposures have been monitored through questionnaires, record linkage and analyses of bio-logical material. By stratifying the genetic analyses by level of environmental exposure, we may have a greater chance of detecting genetic signals. M&C provides the opportunity to examine effects of intrauterine infections and prenatal exposure to specific toxicants and nutritional factors. From the first 20 000 children born into this cohort, we will extract case triads (based on cases of early-onset asthma) and control triads for SNP genotyping. From the Oslo birth cohort, we found that about 4 % of 2-year old children had experienced at least two episodes of bronchial obstruction. This translates to an expected number of cases by age one and a half in the M&C cohort to about 600.

The Oslo Birth cohort started in 1992 with 3754 children born in Oslo to study the natural history of respiratory diseases in childhood and their risk factors. Beside a questionnaire on pregnancy conditions, parents health, socio-economic status and environment information was extracted from the journal and an umbilical cord blood-sample was drawn and serum stored in freezers. The parents were also instructed to collect a sample of house dust, stored at the Norwegian Institute of Public Health. Follow up rate at 2 years was about 80%. At 4 years of age about 3000 of the original families return a new questionnaire. More than 20 papers based on analyses of these data have so far been published, mainly addressing asso-ciations between environmental conditions, atopic predisposition and respiratory symp-toms/diseases the first 4-year of life16. A 10 years follow up has recently been carried out including children from the original birth cohort and children born in 1992 living in Oslo during the autumn of 2001. The data collection includes questionnaire information on health and environmental exposure, spirometry, skin prick tests, and buccal cell samples for DNA extraction. About 3400 children have been tested of which about 2500-3000 is from the original birth cohort. We recently described that for children whose parents were not atopic, environmental tobacco smoke did not seem to increase the risk of early asthma. However, for offspring of atopic parents, a substantially increased risk (relative risk 2.7) was found. This is an example of a possible gene-environmental interaction that we will follow up, using SNP located to candidate genes for atopy. We join an EU application for an IP in the 6th FP: the selection of candidate genes/regions will happen jointly.

5. Breast Cancer

16 Nafstad P, Magnus P, Jaakkola JJK. Early respiratory infections and childhood asthma . Pediatrics 2000a;106:E38 Nafstad P, Magnus P, Jaakkola JJK Risk of childhood asthma and allergic rhinitis in relation to pregnancy complications. J Allergy Clin Immunol 2000b;106:867-73. Jaakkola JJK, Nafstad P, Magnus P. Environmental Tobacco Smoke, Parental Atopy, and Childhood Asthma. Environ Health Perspect 2001;109:579-82. Nafstad P, Oie L, Mehl R, Gaarder PI, Lødrup Carlsen KC, Botten G, Magnus P, Jaakkola JJK, Residential dampness problems and symptoms and signs of bronchial obstruction inyoung Norwegian children. Am J Respir Crit Care Med 1998;157:401-4.

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We have previously demonstrated17 that more than half of all BRCA1 mutation-carrying persons in Norway are descendants from four persons surviving the Bubonic plagues by understanding the population structure, with normal genetic variation, and intrafamilial sharing of haplotypes. This result underscores the power of the proposed techniques.

Half of all families with dominantly inherited breast cancer in Norway are affected in a hitherto unknown gene(s) (BRCAx). The situation is similar in many other Northern European populations. We are engaged in a Nordic collaboration, to identify BRCAx. We are now completing the description of our families with respect to intrafamilial associations in order to exclude the possibility that they are caused by BRCA1/2 mutations. The proposed joint FUGE project is excellently suited to pursue this ambition. The material is available and well characterised, both genealogically and with respect to BRCA1/2, and we know how to analyse the results when obtained. These BRCAx families represent an excellent opportunity for the identification of BRCAx by a genome wide search made possible by the overall project. Data generated within this project will also be shared with our international collaborators. We aim to use SNPs directly for evaluation of candidate genes (CHECK2, ATM, etc), and to directly estimate the maximum contribution of any of these. We also intend to use our large BRCA1 mutation carrying families for direct evaluation of the potential gene-gene interactions of these candidate genes with respect to penetrance (early onset) and expression (breast or ovarian cancer) of the main BRCA1 mutation. This will have immediate effects on the health service given to mutation carriers.

We assume that most of the goals described above would be reachable within a four-year period given a few conditions: We must find a suitable density of polymorphic SNPs in the candidate gene regions. (We have already identified a number of SNPs in the BRCA1/2 region.) Moreover, whether or not we will find BRCAx cannot be foreseen, as it depends on the number of BRCAx genes, linkage disequilibrium in the population and suitable SNPs within the families. These are typical problems in the search for unknown genes with an associated phenotype that is not unequivocally monogenic.

6. Genetic and environmental causes of disease in the male reproductive system

Human toxicology studies the adverse effects that chemical substances may induce, assessing the health risks of exposure in terms of probability of harm and its consequences. Traditionally, human toxicologists used animal-based models to predict effects to humans. A mechanistic understanding of the mode of action of environmental agents is important as a basis for risk estimation, particularly at low dose levels. High throughput methods may be used to analyse interactions with and between specific cellular functions, and they are important for identifying and quantifying potential risks of environmental exposure.

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Disease in the male reproductive system is increasing in the industrialised societies. Time trends and geographical gradients in the incidence of various pathological conditions, like testicular cancer in young men (world’s peak in Denmark and Norway), hypospadia, cryptorchidism and reduced sperm quality (Testicular Dysgenesis Syndrome – TDS), probably result from exposure to some environmental agents, prenatally and as adults18. The responsible factors have not been identified. Genetic factors are contributing to the specific susceptibility of population groups. DNA repair plays a key role in the cellular resistance to environmental and genetic attack. We have reported19,20 that male germ cells from humans and rats repair alkylated DNA efficiently, but they largely cannot remove UV-induced lesions via nucleotide excision repair which also removes bulky adducts (e.g. B(a)P). This has important implications for the transmission of DNA adducts from the paternal genome to the foetus. The presence of B(a)P adducts in early foetuses is related to the smoking habits of the parents, especially of the father21. Concerning oxidative DNA lesion repair, we have shown that human male germ cells are unable to remove oxidised purines but oxidised pyrimidines are repaired normally22. Rat male germ cells show proficient repair of both types of oxidised bases. The species and organ specific differences are illustrated by somatic and male germ cells’ (rats/mice/humans) response to some chemicals (1,2-dibromo-3-chloropropane (DBCP) and other halogenated alkanes). Heavy metals are further examples, since the DNA repair machinery is susceptible to cadmium.

We will develop and use an integrated approach to delineate environmental and genetic factors of importance to male reproduction and fertility. Genetic variations in susceptibility genes (metabolism of exogenous and endogenous compounds) and DNA repair genes will be studied. The suitability of the M&C biobank material for expression analysis has not yet been evaluated and will largely depend on the integrity of blood sample RNA. Feasibility studies will constitute an important and integrated part of this proposal. NIPHs own biobank of 100 frozen normal testicular tissue biopsies will be SNP analysed. Phenotypic characterisation of male germ cells will be carried out. The suitability of the M&C material for functional DNA repair studies will be investigated. M&C questionnaires and biobank material will be analysed to identify possible causes of disease, focussing on TDS associated disease. Cryptorchidism in new-borns will be used as an early indicator of testicular cancer (10 times increased incidence). Lifestyles of mother and father of cases will be analysed. The role of paternal vs maternal effects will be studied. Environmental exposures (PCB, benzo(a)pyrene, heavy metals, acrylamide) will be analysed elsewhere.

7. Statistical methods in functional genomics

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A milestone of this project is the creation of a unitary team in statistics for functional genomics. This group will be able to provide a fundamental service to the Norwegian FUGE community, and will produce innovative methodologies with the aim of reaching within three years international excellence.

Statistics for linkage and association analysis. By studying the joint pattern of inheritance of genetic markers and Quantitative Trait loci (QTL) inference can be made on the location and effects of the genes affecting the trait. The probability that a locus positioned near a marker of known type has a genotype that can be directly associated with the trait can be calculated exactly for simple experimental designs. Via this association, significant QTL/gene associations can be identified for future study. Classical statistical techniques for QTL detection23 (single and composite interval mapping, multiple QTL mapping and variants) are available, but problems arise24 where many false positives typically appear. Multivariate structural equations can be applied to variable pedigrees. Generalized linear mixed models go beyond normally distributed traits. Ordinal scores and discrete traits, like counts of metastases, can have skewed distributions. Bayesian inference25 is becoming commonplace in experimental crosses, now implemented via trans-dimensional MCMC26. Issues of multiple testing can be avoided since the number of QTLs is also estimated, and highly incomplete data27, often arising in natural populations, can be used. Statistical methods are available for case control polygenic aetiology studies with missing data, based on MCMC. Increasing emphasis, particularly relevant to this project, is placed on general population and case-control studies on apparently unrelated individuals, rather than on directly related family members. Merging fine scale SNP mapping with data from other sources (sequence data, expression data, pools etc.) in a coherent way is of crucial importance in the linking of more comprehensive phenotypic data to loci. It is our aim to implement and further develop the state-of-the-art of these statistical methods, so to apply the most adequate approach in each context. We will investigate new classical and Bayesian models. The analysis of complex phenotypes is hampered by the absence of user friendly estimation tools. We propose to extend available public domain libraries (like R) and the GLLAMM framework28 to include multipoint linkage and association analysis, multivariate phenotypic and censored data. Distinguishing environmental effects from genetic inheritance is a further important aspect of this project. This is difficult since population subgroups share both the putative disease gene and a high risk of the disease for environmental reasons. In case-triad association studies, as the M&C cohort, parents act as controls. We have extended log-linear models for triads to include highly polymorphic loci. Following the coarse genomic SNP map, we want to analyse small regions of densely spaced SNPs. We will compute the most likely “SNP haplotype” of the individuals also when phase is unknown. This would allow us to consider the haplotypes as “alleles” or allele equivalents29. We shall estimate relative risks for the different alleles, rather than just testing associations. We will study gene-environment and gene-gene interactions and we will propose methods to evaluate separately the parental genetic contributions. For most of the above methods, knowledge of the distribution of markers allows for better estimates. An important issue arises in intrafamilial associations. All methods are based upon the fact that large blocks (haplotypes) of DNA are passed unbroken from parents to children. Any normal variation within such blocks will be unbalanced between affected close relatives. Within families, SNP information will be used to create haplotypes. The haplotypes are regarded as polymorphic markers. Excess/lack of haplotype sharing between affecteds in the same kindred indicates a causative genetic factor within the haplotype. Such “affected sib sharing” methods will be used where relevant.

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A coherent model for microarray data. RNA expression data are observable phenotypes resulting from a genotype. The analysis of microarray data is based on many separate operations: imaging; normalisation to compensate for dye effects and other experimental disturbances; analysis, based on standardised intensity ratios or ANOVA; identification of the differentially expressed genes, often by testing or clustering. The current practice is to perform these operations in sequence, where plugging-in results in the next step. There is no memory of the assumptions made and plugged-in’s are assumed to be exact, with no propagation of the uncertainty they carry. Our ongoing aim30 is to build a statistical model that incorporates in a single inferential task, all steps, thus propagating uncertainties, and modelling dependencies so that realistic confidence levels can be attached to the final conclusions. Multilevel and Bayesian hierarchical models, computationally intensive methods, like MCMC, will be investigated. Model based analysis ultimately does not require labelling with two dyes. This approach has relevance to cDNA-based arrays as well as for instance protein arrays, where dual labelling may be difficult. Putative pathways will be interpreted as a priori correlated genes and tested by means of model comparison.

Joint analysis of markers, gene expression, environmental factors and clinical phenotype data. Current practice is to first identify genes with explanatory power for some disease (or treatment effect) and then use these as covariates in regression (Cox proportional hazards) with survival response31. This approach is unsatisfactory32 since gene expression observations have substantial measurement errors that are not corrected for, leading to falsely small p-values. We aim to develop an approach where survival/treatment data guide the identification of interesting genes, turning the inferential flow in the other direction. Complicating aspects include censoring, surrogate endpoints, highly correlated covariates, as for example gene expression and gene copy numbers. The proportional hazards assumption, basis of Cox analysis, is often invalid. An alternative, additive approach has been developed33. It easily allows for covariate adjustment of event histories in a Markov setting. Frailty models for heterogeneity in multivariate survival34 will be investigated. Data compression35 is important to create gene related covariates of low dimension. Classical and Bayesian approaches will be investigated, including shrinkage and regularization36. Direct use of marker loci as covariates will also be investigated.

Time dynamics of gene expression. Time course gene expression data are available for thousands of genes measured simultaneously, describing the dynamics over time after treatment or during disease progression. Traditional time series techniques are inadequate. Clustering techniques have been proposed but they have fail to address the time structure37. It has been proposed38 to fit a latent, stochastic difference equation with MCMC based Bayesian inference. We want to apply and extend this method to data of clinical interest. Patients typically go through many stages of a disease, the monitoring of which leads to time-to-event data. We want to use expression data to modulate changes over stages. Hidden Markov models with inhomogeneous transition probabilities are relevant here.

Microarray-based comparative genomic hybridisation . Gene amplification is frequently observed in cancer as an increase in copy number of a specific chromosomal region with a net gain of genetic material. Genome-wide studies of gene amplification are possible by methods such as conventional comparative genome hybridisation (CGH) approaches or by microarrays. Gene amplification may itself lead to overexpression of genes, or it may interact with other factors to cause overexpression in trans. How precisely can over-expression of a gene be related to gene amplification, and can gene amplification alone explain observed overexpression? Preliminary studies39 indicate that there is considerable

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correlation between copy number and expression. Copy numbers of nearby regions of the chromosome may be highly correlated and that the exact position of (and hence distance between) chromosomal regions probed for gene amplification is not always known.

9. Bioinformatics and distribution of results

The project will have a web site maintaining all produced information. Statistical and linkage tools will be available and critically documented. The database will allow for import and export of data, with tools to combine selected highly mapped regions with others. The web page will present new discoveries in a form accessible to the general public, also to make genetic research understandable to the Norwegian media.

10 What will come next

This proposal aims to start making full use of the research possibilities offered by four FUGE platforms, Biobanks, SNP genotyping, Microarrays and Bioinformatics. We will rapidly be able to show results on gene frequency in the M&C mothers, as well as several case-control studies. The proposal is also crucial for establishing a strong and large enough research group in genetical statistics. However, this is just the beginning. The Norwegian setting is unusual in that so much effort has been devoted to providing epidemiological cohorts with stringent data on phenotypes and environmental exposures. Gene signals are more easily understood when control is made for environmental exposures. The long term aim is to have en extensive genotyping of all 280000 subjects in the M&C study, though funding for this seems prohibitive. Already within the limited economy of this project, we open for the possibility to perform important epidemiological and clinical studies and very basic studies in population genetics. We can study linkage disequilibrium, recombination patterns, prenatal selection against genes as well as the occurrence of de novo mutations. By establishing a genealogical research database for the whole of Norway, larger families can be created from the M&C dataset, enabling us to compare genetic patterns (haplotypes) within and between families and compare families residing in different regions of Norway. Migrational history and gene drift can be elucidated. We plan to have the anonymized data on gene frequencies in the public domain, but keep the personal identification tag so that phenotypes of descendants can be followed in time and studied with respect to genotypic pattern as well as gene expression phenotypes.

11. The scientific team

This proposal involves many scientists, senior and junior, with different expertise, a common aim and coordinated specific projects. (See the Appendix for a list of named scientists who will join the research activity of this project.) The projects will foster many new collaborations across institutes and disciplines. The high level of interdisciplinarity is a key aspect, with experts in genetics, toxicology, medical sciences, statistics, epidemiology and molecular biology. We will devote time and efforts to make aims, tools, technologies, methods and results a common resource. Training and education, beyond the traditional boundaries of disciplines will include common supervisions, seminars and collaborative research. The participating scientists can be subdivided into three complementing groups.

The Oslo clinical genomics team at the Norwegian Radium Hospital (DNR) has comprehensive knowledge on the instrumentation and handling of genomic scale projects on both the lab and informatics side. We are completing a survey of BRCA1 mutation

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frequencies in a sample of 3000 individuals based on serum DNA. We have extensive knowledge in mutation analysis, genetic mapping and analysis, and will handle basic SNP typing needs with a combination of in house resources and the CIGENE FUGE platform.

The Oslo health genomics team at the National Institute of Public Health (NIPH) has performed a series of epidemiologic studies including biobanks and has competence in phenotyping and measurements of environmental exposure. The team runs the Twin panel and has expertise in quantitative genetics. The Medical Birth Registry is part of NIPH.

The Oslo statistical genomics team is an active group of statisticians with research projects in functional genomics. In collaboration with various hospitals and universities, research is underway on, among others, microarray data, SNP and QTL linkage, clinical genomics. We are affiliated to five different institutes on the Blindern campus. An objective of this project is to strengthen common activities within statistical genomics (seminars, guests, working groups, co-supervision of students), as part of the Oslo statistics community that has a high international status. A proposal for a Norwegian centre of Excellence (SFF) in Statistics was selected to the second round out of 129. A major part of that proposal was directed towards medical sciences. The centre was not funded, despite excellent referee reports. We will provide a coordinated nationwide facility in statistical genomics.

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Members of our team join applications to the 6th EU FP: Statistics for Epidemiology and Population dynamics (StEP, principle investigator Gareth Roberts), Genotopia (Stephen Holgate), Novel approaches and applications of protein arrays (ProArray, Kirill Alexandrov), Heatox (Kjerstin Skog), Childrengennotox (Lisbeth E. Knudsen).

12. Organisation and funding needs

A physical centre for this project will be organised at the section of medical statistics, UiO. This center will promote regular meetings among all participating groups, with formal and informal seminars, crossdisciplinary workshops aiming to generate joint projects and to consolidate research activities, interdisciplinary brainstorming sessions and more. There will be a programme with guest scientists, activities specially aimed to students, meetings with commercial actors and platform scientists. We will also organise events open to the general public. UiO is evaluating to build a new part of the medical campus: we will follow up this possibility. Because of the high level of interdisciplinarity, the project will have a leading group with Arnoldo Frigessi, Eivind Hovig and Per Magnus. As key scientists they have broad expertise in different fields, strong scientific records and coordination capacities. A scientific advisory board will be appointed, in connection to the existing platforms and the FUGE programme. The total required budget is 59 million Kr. including 40 million Kr in total over 4 years from FUGE. See the Appendix for details.

Oslo, 13 June 2003Arnoldo Frigessi, Eivind Hovig, Per Magnus

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APPENDIX

Figure 1: Rationale.

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Preeclampsia

DNA variation – Mother & Child cohort

Statistical methods

Asthma Breast cancer Disease in the male reproductive systemBioinformatics

Interdisciplinary working groups

Web

Interdisciplinary training

Statistical competence in functional genomics

A. Participating groups with key scientists

Units and key researchers (*)Norwegian Institute of Public Health (NIPH) Biostatistics

Håkon Gjessing Andres Skrondal

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NIPH Dept. Chemical Toxicology Gunnar BrunborgNur Duale

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NIPH Dept. Genes Environment Per Magnus Kjersti Skiold Rønningen

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Norwegian Computing Centre (NR) Marit HoldenMagne Aldrin

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The Norwegian Radium Hospital (DNR) Eivind HovigPål Møller

5

University of Oslo (UiO) Dept. Computer Science (IFI)

Knut LiestølOle Chr. Lingjære

5

UiO Dept. Mathematics Ørnulf BorganIngrid K. Glad

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UiO Section of Medical Statistics Arnoldo FrigessiOdd Aalen

5

(*)Total number of scientists in FUGE research

This project will also connect with Norevent - a Thematic Research Area on survival and event history analysis, Faculty of Medicine, UiO. Norevent is one of 13 prioritised areas at the Medical faculty, see http://www.med.uio.no/imb/stat/norevent/.

We are currently collaborating in Norway and internationally with many groups in biostatistics, epidemiology, and medical sciences. We wish to mention here Rolv Terje Lie (Bergen), Juni Palmgren (Stockholm), Hans van Houwelingen (Leiden), Elja Arjas (Helsinki), Sylvia Richardson (Imperial), Allen J. Wilcox (NI EHS, USA), Clarice R. Weinberg (NIEHSUSA), Mark van de Wiel (Eindhoven), Petter Mostad and MatsRudemo (Gothenburg), Diana Anderson and Martin Brinkworth (Bradford, UK), Rigmor Austgulen (NTNU), William Thilly (MIT), Åke Borg (Lund), the Nordic Breast Cancer Study Group, Richard Cotton, president of the Human Genome Variation Society, Steven A. Narod (Toronto), Hans F. Vasen (Leiden), Andrew Collins (UiO), Niels Keiding (Copenhagen), Rob Henderson and Peter Diggle (Lancaster), Haavard Rue (NTNU).

B. The Norwegian Mother and Child Cohort Study (M&C)

This ambitious projects aims to find causes of diseases by recruiting 100 000 pregnant women to take part in a follow-up that may last for many years after childbirth. By May 2003, 25,000 women have been recruited with a response rate of about 50%. M&C is a cohort study, which implies that exposures are measured at the time they exert their effects, often a long time before overt disease develops. Both the preeclampsia and asthma/allergy projects outlined below are based on nested case-/control studies within M&C. The women are invited to the study about two weeks before the routinely performed ultrasound examination (about 17th week) in pregnancy. They receive an envelope containing an information folder, two questionnaires and a consent form. The questionnaires have focus on nutrition and other exposures as well as health history. Monitored exposure variables include genes, psychosocial factors, infections, use of medication, nutrition, life styles, occupation, use of health services, substance abuse, socio-economic factors, as well as

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chemical and physical factors in the environment. Health variables include maternal and paternal history and health outcomes for the mother and child detected during and after pregnancy. The cohort design allows nested case-controls studies to be conducted when additional exposure data or blood samples are collected. The women are informed of the voluntary nature of the project and the possibility to withdraw from the study at any time. The first blood sample is taken at the same time as the ultrasound examination. More than 90 % of fathers accompany their partner to this examination, and fathers are asked to give a separate consent. If the fathers agree, a blood sample will also be taken from them and they will fill in a short questionnaire on their own health, medication and occupational exposure. About 80 % of partners of recruited women also consent to participation. A third questionnaire (other exposures in pregnancy) is sent to the pregnant women around week 30. Soon after birth a blood sample is taken from the umbilical cord and the second sample is taken from the mother. All blood samples are sent to a biobank for processing and storage. Blood is sampled in two 7.5 ml EDTA-tubes from the mothers and fathers at the time of ultrasound. One tube will be centrifuged locally so that plasma can be sent to Oslo to be frozen in aliquots. The remaining cells as well as the other tube is sent to Oslo for DNA extraction and storage at – 20 C (for DNA) and –80 C (for plasma and whole blood) in aliquots. Two 7.5 ml tubes with blood from the umbilical cord is sent without processing locally. DNA is extracted using a kit (PureGene, Gentra). Approximately 150 – 700 mg DNA is obtained from each sampling. After the DNA is diluted, all samples have the same concentration, 100 ng/l, and a robotic system pipettes out 4 aliquots of 1,5 ml each into deep-well plates. A questionnaire on maternal and child health status is sent to the women when the child is 6 months, 18 months and 6 years. Health outcomes are also collected from hospital discharge registries as well as other health registries, such as the Medical Birth Registry, the Cancer Registry, the Diabetes Registry and the Cause of Death Registry. For some of the subprojects, additional data collections for exposures and outcomes are made.

C. Support of 4 FUGE platforms

This project is linked to four FUGE technology platforms. We have applied for their support, which will be forwarded separately:

Biobanks For Health. The Norwegian Bioinformatics Platform The Norwegian Centre for Integrative Genetics – CIGENE The Norwegian Centre for Microarray Technology

D. Support of participating organisations

A letter of support from each institution is enclosed (A single letter covers both NIPH and Biobank, and will be sent separately.) Own funding is provided according to the enclosed table.

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Self Funding

Unit Self funding per year (kKr)

Specification

The Norwegian Radium Hospital (DNR)

980 Research time, robotics, MegaBace 50% scientist, 50% engineer

NIPH Dept. Chemical Toxicology 440 Research time, lab equipment, facilities and personell

NIPH Dept. Genes Environment 1000 Research time, lab equipment, facilities and personell, biobank M&C

Norwegian Computing Centre (NR) 550 Research time, computers and software, 50% PhD

UiO Dept. Computer Science 500 Research time personell, computers, library, secretarial help, 50% PhD

UiO Dept. Mathematics 650 Research time personell, computers, library, 50% PhD

UiO Section of Medical Statistics 500 Research time personell, computers, library, secretarial help, 50% PhD

Total of self funding, per year 4620

F. Ethics

The study of the causes of the disorders described here is of large societal interest, since diseases such as asthma/allergies and breast cancer cause substantial suffering. The Norwegian M&C Cohort Study includes an informed, written consent. This states that the information gathered will be used solely for research into the causes of disease, including the study of genetic factors. No information about risk of disease based on results from the analysis of biological material will be given back to the participants, unless they take part in new subprojects which will require a new consent. The M&C study has been approved by a regional ethics committee for medical research, and by the Data Inspectorate. Since no interventions are performed, there is little concern for damage to the participants. Genetic information will not be accessible for insurance companies or employers.

G. Cost estimates and budget

For the core genotyping, we intend to make 60 pools with 1 000 individuals each. These pools will be typed using our in house MegaBace platform, modified for allele separation using denaturing temperature cycling. The platform has a published documented sensitivity of detecting one allele in 2 000 alleles. This carries a cost of 1 000 primers pairs (half of which with extended length due to GC clamps), with a price of ca.1 000 NOKs per pair, i.e. 1M NOKs. The number of primers is based on taking the DeCode SNP frequencies as starting information, with the expectation that these will likely be more

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relevant than other SNP databases for the Norwegian population. Other operating costs are estimated to 400 000 NOKs per year, including plasticware for robotics, gel matrix material, capillaries and nucleotides. Relevant enzymes are produced in house. An independent verification and validation effort will be performed by the use of the SNP platform, where 100 individuals will be genotyped for all selected SNPs in parallel with a pool of all 100 individual using the in house platform. The approximate prices quoted by the SNP platform are 8 NOKs per SNP and 16 NOKs per STRP. Using candidate gene SNPs as a basis, this will require a total of 640 000 NOKs. As the primers for the SNP platform will be made for larger samples, this opens for the use of these for core typing of individuals within subprojects to a somewhat lower price. The work force necessary for laboratory work is estimated to one half laboratory assistant, and SNP selection and data processing provided as self cost. Each subproject has a specifically calculated budget. For shortness we report here only that related to Breast Cancer:

Part 1: 200 families with known BRCA1 mutations, using affected sib method with 5 individuals per familiy= 1000 individuals typed for 6 SNPs on 10 candidate genes, using the Ås platform: 8 NOK x 6 SNPs x 10 genes x 1000 individuals= 480 000 NOKs

Part 2: 300 BRCAx families using affected sib method as above: 720 000 NOKs. The same families using a genome wide scan on in house platform: 800 SNPs x 300 individuals (families) x 2 NOK =480 000 NOKs

Sum part 1+2= 1 680 000 NOKs over four years= 420 000 per year

Research Institute Purpose 2003 2004 2005 2006 TotalBudget core typing DNR Primers 1000 1000 1000 1000 4000

CIGENE FUGE Platform SNP 640 640 640 640 2560

DNR Lab assistant (50%) 200 200 200 200 800

DNRConsumables, hard &

software 400 400 400 400 1600

  Total SNP 2440 2440 2440 2440 9760Central data base management, web, travel & guests All

Travel, guests, seminars,

publications, web 700 700 700 700 2800

Statistical genomics NR Research 800 800 800 800 3200

Medstat 1 post-doc 633 633 633 1899

Medstat Professor II (20%) 400 400 400 400 1600

IFI 1 PhD 535 535 535 1605

IFI PhD abroad 100 100

IFI Lab Technician 200 200 200 200 800

Matem. Inst. 1 PhD 535 535 535 1605

Matem. Inst. PhD abroad 100 100

Matem Inst. 2 MSc support 190 190 190 190 760

NIPH 1Post doc 633 633 633 1899

NIPH Lab Technician (50%) 200 200 200 200 800

NIPH Researcher (20%) 180 180 180 180 720

  Total Statistics 3236 4306 4506 3040 15088

Preeclampsia NIPH Post doc 633 633 633 1899

NIPH SNP(candidate genes) 200 200 400

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NIPH Research nurse (20%) 80 80 80 240

  Total Preeclampsia 713 913 913 2539

Asthma NIPH PhD 535 535 535 1605

NIPH PhD abroad 100 100

NIPH SNP(candidate genes) 200 200 400

NIPH Consumables 100 100 200

NIPH Research nurse (80%) 300 300 300 900

  Total Asthma 1135 1235 835 3205

Breast Cancer DNR SNP(candidate genes) 420 420 420 420 1680

DNR Research nurse (50%) 180 180 180 180 720

DNR Lab assistant 400 400 400 400 1600

  Total Breast Cancer 1000 1000 1000 1000 4000Disease in the male reproductive system NIPH Post doc 633 633 633 1899

NIPH SNP(candidate genes) 150 150 300

NIPHConsumables, questionnaires 100 100 100 300

NIPH Microarrays 200 200 200 600

  Total reprod. diseases 450 1083 933 633 3099

Total Project All Total  9674 11677 11327 7813 39961

H. Commercial implications

The project will provide the coarse map as a public resource. As the project is a basis for both academic and commercial utilisation of the generated biobank data, it is of importance to stimulate commercial utilization through agreements with all willing data providers. This will hopefully lead to a much needed stimulus for the national biotech industry, in providing new business concepts, and by stimulating a faster development path for new commercialisation. The scalability of the proposed project also implies that specific parts may be defined in a commercial domain, without hindrance to the general project aims. We intend to actively seek inclusion of commercial partners. PubGene AS, a commercial com-pany that develops databases, software tools for genomic and proteomic studies and drug discovery, has already agreed in supporting our project, and to jointly identify commercial aims. More partners are to come. Results related to each subprojects remain within the institution to which the correspondng researchers are affiliated and follow their IPR rules. Doubtful cases will be treated by the leading group with support from institutions.

I. CV of three leading scientists

Arnoldo Frigessi

Born in Milano, Italy, on the 13th of April 1959. Italian citizen.

Education, scientific visits and positions

1983 Italian Laurea in Mathematics, University of Milano.1986 – 1992 Researcher of the Istituto per le Applicazioni del Calcolo (IAC), Roma.1992 – 1994 Ass. Professor in Probability and Mathematical Statistics, Univ. of Venezia1994 – 1997 Associate Professor in Statistics, Third University of Rome1997 – Aug. Senior Researcher at the Norwegian Computing Centre.

2003 Chief Research Scientists since 1999.Aug. – today Professor in Statistics, Section of Medical Statistics, University of Oslo

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2003 Adjunct research scientist, Norwegian Computing Centre

Professor competence in statistics, University of Oslo (1996), NTNU Trondheim (1997).Adjunct Professor (20% position), Department of Mathematics, UiO, since 2000.

Longer research visits: Department of Mathematics, TU Delft, 6 months (1985); Institute of Mathematics and Statistics, University of Sao Paulo, 3 months (1990); Department of Statistics, Rutgers University, 1 year (1991); NTNU Trondheim, 6 months (1995).

Teaching and student supervision

I have been teaching courses at various levels in: Statistics, Random Fields, Statistical Image Analysis, Probability Theory, Stochastic Processes, Graphs, Markov Chain Monte Carlo, Extreme value theory in actuarial and environmental sciences, Stochastic simulation.

Ph.D supervisions and co-supervisions: Paula Gonzaga de Sa', Univ. Louvain, 1991-93, Approximated image restoration. Fabio Divino, U. Florence, 94-6, Penalised pseudolikelihood in spatial statistics. Marco di Zio, University of Rome I, 1996-98, Wavelets: theory and applications. M Rita Sebastiani, U. Florence, 96-8, Spatial identification of local labour markets. Xeni Dimakos, UiO, 1998-2000, Topics in computer intensive inference Sveinung Erland, NTNU Trondheim, 1999-2003, Adaptive MCMC. Turid Follestad, NTNU Trondheim, 1999-2003, Modelling in space and time. Inge Olsen, NTNU Trondheim, 2002-2005, population dynamics. Håvard Rue, NTH, Trondheim, 1992-93, Topics in Image Analysis; Knut Heggland, Oslo, 2000-2003, Simulation based inference.

Supervision of post-docs: Angela Mariotto, Italian Research Council, 1990; Laurent Younes, Italian Research Council, 1991; Julian Stander, British Royal Society, 1992--93; Clare Marschall, EU TMR Spatial and Computational Statistics, 1999-00; Mark van de Wiel, EU TMR Spatial and Computational Statistics, 2001.

Member of PhD committees in Norway, Belgium, Denmark, Finland, Sweden.Supervision of 9 students in their thesis work for the Italian Laurea and Norwegian master.

Current research grants, editorial activity and other scientific work

Project leader of the Strategic Institute Project "Statistical Analysis of Risk- StAR", Norwegian Research Council (NFR), 2003-2007, 16 million kroner. NR and Dept. Mathematics UiO. Focus on: health risk, environmental risk, industrial risk, financial risk.

Current research grants: EU STEPICA: The Plague in Central Asia, 2001-2003. NFR BeMatA: Structured Stochastic Models in Biological Marine Systems, 2000-4. NFR BeMatA: Statistical model selection, 2003-2006. FUGE grant: Bioinformatics Oslo platform, 2003-2007 FUGE grant: Clinical bioinformatics, E. Hovig principal researcher, 2003-2005.

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NFR grant CIGENE, Agriculture University, S. Omholt, principal researcher, 2003-7.

Associated editor of the Scandinavian Journal of Statistics, since 2000.Associated editor of the Journal of Applied Statistics in Business and Industry, since 2002.

Chairman, organising committee, 25th European Meeting of Statisticians in Oslo, 2005.Member of the local organising committee, Nordic Meeting, International Biometric Society, 2005 Member of the scientific committee, 13th conference on Mathematics for Industry, Eindhoven, 2004. and of the 12. Norske Statistikermøte, 2003.Member of the committee, Norevent, area of special research interest, medical faculty UiO.

Main lectures in the current year (2003): Aarhus, Workshop on Statistical Aspects of Microarray data, Feb. 2003, invited Wye (UK), Gene Expression workshop, July 2003, invited Puerto Rico, 1st Joint Meeting IMS-IBSA, July 2003, invited Berlin ISI conference Berlin 2003, Oslo, Workshop on Statistical challenges from genetics, Sept. 2003, invited

Main past research grants and research activities

Project leader Strategic Institute Project "Knowledge, Data, and Decisions", NFR, 1998-2002, 1,7 million Euro. The project involved about 30 statisticians.

Member of the steering committee of the scientific programme on "Highly Structured Stochastic Systems", European Science Foundation for 1993-1995 and 1997-2000. Member of the scientific committee of the EU TMR programme, "Spatial and Computational Statistics", 1997-2001. The programme had a budget of circa 19 mil. Euro, for its 7 international nodes. I was leader of the Rome-Oslo node.

Collaboration with the Microarray Consortium and the Norwegian Cancer Hospital on microarray analysis, with a grant for 2002 and 2003.

I have been writing the proposal for a Norwegian centre of Excellence in Statistics (SFF), in collaboration with key scientists at UiO. This proposal was selected to the second round among the 40 ones chosen out of the 129 submitted ones. A major part of the proposal was directed towards medical sciences. I was the intended leader of the centre, with a budget of 20 mil kr. annually for ten years. The centre was not funded, despite excellent judgements.

Referee for several journals: J. of Applied Probability, Annals of Statistics, Annals of Applied Probability, Biometrika, J. of the Royal Statistical Society (B and C), J. of Statistical Physics, Scandinavian J. of Statistics, J. of the Italian Statistical Society, Statistics and Computing, Stochastic Models, Advances in Applied Probability.Member of the European Regional Committee, Bernoulli Society (1999-2002, programme co-ordinator 1999-2000); Steering Committee,European Network for Business and Industrial Statistics (2001-2002); Norwegian Council for Mathematics (2000-2002).

International reviewer of the British EPSRC programme for statistical research (2000). Referee of research projects for the National Science Foundation (USA), the Israeli Science Foundation, the Swedish Research Council, the Finish Academy of Sciences, the Irish

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Research Council. World Health Organisation Temporary Adviser on the BIOMED project Disease Mapping and Risk Assessment (1998). Chairing the group on Ecological Analysis.

Editor (with Piero Barone and Mauro Piccioni), Lecture Notes in Statistics, Springer, vol. 74, 1992, "Stochastic models, statistical methods and algorithms in image analysis".Editor (with Nils Lid Hjort) of a special issue of the Journal of Nonparametric statistics dedicated to "Statistical models and methods for discontinuous phenomena", 2001.

Member of the scientific committee of 15 international conferences and workshops. I have given more than 65 seminars and invited lectures in more than 15 countries.

Refereed publications (selection of last 5 years)

1. M. di Zio and A. Frigessi, Smoothness in Bayesian Nonparametric regression with wavelets, Methodology and Computing in Applied Probability, vol. 1, 395-409, 1999.

2. Biggeri A, Divino F., Frigessi A., Lawson A., Bohning D., Lasafre E., Viel J.-F. Introduction to Spatial Models in Ecological Studies, in Disease Mapping and Risk Assessment for Public Health, Wiley & Sons, 181-201, 1999

3. F. Divino, A. Frigessi and P. J. Green, Penalised pseudolikelihood estimation in Markov random field models, Scandinavian J. of Statistics, vol 27, n.3., 445-458, 2000.

4. A. Frigessi, J. Gåsemyr and H. Rue, Antithetic Coupling of two Gibbs Sampler Chains, Annals of Statistics, 2000, vol 28, n.4, 1128-1149

5. Ø. Skare, F.E. Benth and A. Frigessi, Smoothed Langevin proposals in Metropolis-Hastings algorithms, Statistics and Probability Letters, vol. 49, 345-354, 2000.

6. X. Dimakos and A. Frigessi, Hierarchical Bayesian Premium Rating with Latent Structure, Scandinavian Actuarial Journal, 2002, 3, 162-184.

7. Hisdal, H., Tallaksen, L.M. and Frigessi, A. Handling non-extreme events in extreme value modelling of droughts. In: FRIEND 2002-Regional Hydrology: Bridging the Gap between Research and Practice (ed. H. van Lanen , S. Demuth). IAHS, 274, 281-8

8. G. Storvik, A. Frigessi and D. Hirst, Space-time Gaussian fields and their time-autoregressive representation, Statistical Modelling, 2002, 2, 139-161.

9. A. Frigessi and N. L. Hjort, Statistical models and methods for discontinuous phenomena, Journal of Nonparametric Statistics, 2002, 4, 1-5.

10. A. Frigessi, O. Haug and H. Rue, A dynamic mixture model for unsupervised tail estimation without threshold selection, Extremes, 5 (3), 219 – 236, 2002.

11. K. Heggland and A. Frigessi, Choosing estimating functions in Indirect Inference, accepted subject to revision by Journal of the Royal Statistical Society, series B, 2003.

12. A. Frigessi, On some current research in MCMC, in Highly Structured Stochastic Systems, Green, Hjort & Richardson eds. Oxford University press, 2003, 1-5

Per Magnus

Date of birth: August 16, 1951Basic education: 1976: MD, University of OsloCurrent position: Head of Department of Genes and Environment (since 2002), Division of

Epidemiology, Norwegian Institute of Public Health, Academic title Professor II (20 % position) in Community Health, Institute of General

Practice and Community Medicine, University of OsloDegrees: 1985: PhD, University of OsloPrevious positions: Research fellow and associate professor at the Institute of Medical Genetics

1977-1984. Intern, Department of Pediatrics, Central Hospital of Akershus

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1984-1985. Epidemiologist from 1985, and from 1992-2001 head of Department of Epidemiology, National Institute of Public Health, Oslo

My research interest has generally been in etiological research, first focused on reproductive outcomes, disentangling the effects of maternal and fetal genes using the methods of quantitative genetics. From 1985, I have had a more broad interest in gene-environment interaction and community medicine, initiating and participating in studies of sexual behaviour, HIV/AIDS, asthma/allergies, bronchitis, preeclampsia and birth weight.

Many projects include collaboration with research groups in other European countries and the USA. I have published and coauthored about 200 papers in peer-reviewed journals since 1979.

Selected publications since 2000:

1. Stene LC, Ulriksen J, Magnus P, Joner G. Use of cod liver oil during pregnancy associated with lower risk of type 1 diabetes in the offspring. Diabetologia 2000;43:1093-8.

2. Hagen JA, Nafstad P, Skrondal A, Bjørkly S, Magnus P. Associations between outdoor air pollutant and hospitalization for respiratory diseases.Epidemiology 2000;11:136-40.

3. Hetlevik Ø, Pløen Ø, Nystad W, Magnus P. The wheezing schoolchild – an undiagnosed asthmatic. A follow-up of children with parentally reported episodes of wheeze without diagnosed asthma. Scand J Prim Health Care 2000;18:122-6.

4. Nafstad P, Magnus P, Jaakkola JJK. Early respiratory infections and childhood asthma. Pediatrics 2000;106:e38.

5. Amundsen EJ, Aalen OO, Stigum H, Eskild A, Smith E, Arneborn M, Nilsen Ø, Magnus P. Back-calculation based on HIV and AIDS registers in Denmark, Norway and Sweden 1977-95 among homosexual men: estimation of absolute rates, incidence rates and prevalence of HIV. J Epidemiol Biostat 2000;5:233-43.

6. Glinianaia SV, Skjærven R, Magnus P. Birthweight percentiles by gestational age in multiple births. A population-based study of Norwegian twins and triplets. Acta Obstet Gynecol Scand 2000;79:450-8.

7. Cinek O, Wilkinson E, Paltiel L, Saugstad OD, Magnus P, Rønningen KS. Screening for the IDDM high-risk genotype. A rapid microtitre plate method using serum as source of DNA. Tissue Antigens 2000;56:344-9.

8. Stene LC, Magnus P, Lie RT, Søvik O, Joner G and the Norwegian Childhood Diabetes Study Group. Birth weight and childhood onset type 1 diabetes: population based cohort study. BMJ 2001;322:889-92.

9. Nafstad P, Magnus P, Gaarder PI, Jaakkola JJK. Exposure to pets and atopy-related diseases in the first 4 years of life. Allergy 2001;56:307-12.

10. Stene LC, Magnus P, Lie RT, Søvik O, Joner G, and the Norwegian Childhood Diabetes Study Group. Maternal and paternal age at delivery, birth order, and risk of childhood onset type 1 diabetes: population based cohort study. BMJ 2001:323:369-71.

11. Magnus P, Gjessing HK, Skrondal A, Skjærven R. Paternal contribution to birth weight. J Epidemiol Comm Health 2001;55:873-7.

12. Magnus P, Eskild A. Seasonal variation in the occurrence of pre-eclampsia. Brit J Obstet Gynaecol 2001;108:1116-9.

13. Eggesbø M, Botten G, Halvorsen R, Magnus P. The prevalence of CMA/CMPI in young children: the validity of parentally perceived reactions in a population-based study. Allergy 2001;56:393-402.

14. Stene LC, Magnus P, Rønningen KS, Joner G. Diabetes-associated HLA-DQ genes and birth weight. Diabetes 2001;50:2879-82.

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15. Trogstad LIS, Eskild A, Magnus P, Samuelsen SO, Nesheim B-I. Changing paternity and time since last pregnancy; the impact on pre-eclampsia risk. A study of 547 238 women with and without previous pre-eclampsia. Int J Epidemiol 2001;30:1317-22.

16. Stene LC, Rønningen KS, Magnus P, Joner G. Does HLA genotype influence relative risk of type1 diabetes conferred by dietary factors? Diabet Med 2002;19:429-31.

17. Vangen S, Stoltenberg C, Skjaerven R, Magnus P, Harris JR, Stray-Pedersen B. The heavier the better? Birthweight and perinatal mortality in different ethnic grouops. Int J Epidemiol 2002;31:654-60.

18. Hwang B-F, Magnus P, Jaakkola JJK. Risk of specific birth defects in relation to chlorination and the amount of natural organic matter in the water supply. Am J Epidemiol 2002;156:374-82.

Eivind Hovig

Born 18 September 1953, Oslo, Norway. Norwegian citizen.

Education and academic degrees1984: Candidatus realium, Institute of General Genetics, University of Oslo. 1992: Doctor of philosophy, Medical Faculty, University of Oslo

Present position: Senior scientist at The Norwegian Radium Hospital, Institute for Cancer Research, Department of Tumor Biology.

Research visits1983: 3 months at the Wallenberg-laboratory in Stockholm with Dag Jensen:

Metabolic co-operation in the V79/HPRT-system.1989-90: 3 months at TNO, Rijswijk, the Netherlands with Dr. André Uitterlinden

and Professor Jan Vijg: 2-Dimensional DNA electrophoresis.1999: Six weeks at Dr. Edison Liu’s laboratory at the NCI, Gaithersburg, USA.

Microarrays applied to metastasis questions.

Awards2001: Dr. Ragnar Mørks award for scientific excellence. 2002: Diploma SND Inventor prize

Publications55 publications in international refereed journals, with more than 1700 citations.

This years invited lecturesAdvanced topics in microarray analysis. National Institute of Health, Jan 22, 2003.Tannlege Aase og frues memorial lecture. Onkologisk Forum Nov. 23 2000, Tromsø.

PatentsFodstad, Ø., Engebråten, O., Hovig, J.E., Ree, A.H. (1997) Immunomagnetic cell separation used in identification of genes associated with site-preferenced cancer metastasis formation, PCT/NP97/00083.Rye, P., Hovig, E., Bovin, N. (1997) Novel matrix for affinity electrophoresis of carbohydrate binding proteins, Patent 971876, Norway.

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Fodstad, Ø., Hovig,E., Engebraaten,O. Mælandsm,G., Agrawal,S. (1998) CAPL-specific oligonucleotides and method of inhibiting metastatic cancer, US. Patent #: 5,789,248, 5,877,308 and 5,872,007.

Patents appliedHovig, E., Skretting, A., Kvinnsland, Y., Breistøl, K., Nygård, E., Yoshioka, K. Method and apparatus for characterizing a DNA array. Norwegian and US patents applied, 20001.Jenssen, T.-K. and Hovig, E.: System for Analysing Occurrences of Logical Concepts in text Documents. UK Application No 60/342,682, 2001.

Teaching experienceHave supervised two doctoral students (Gunhild Mælandsmo (NFR) and Turid Mellingsæter (NFR)), two diploma engineers (Karen-Marie Heintz and Berit Bjugan, University of Tromsø), one medical student (Frank Pettersen, University of Oslo), and one student at Norsk landbrukshøgskole (Siri Tveito). Presently supervising doctoral students Fang Liu, Lina Cekaite and Sigurd Bøe, and co-supervising three doctoral students, Kristin Andersen (with Gunhild Mælandsmo, Vigdis Nygaard (with Øystein Fodstad) and Anja Gulliksen (with Reidun Sirevaag and others). Teaching at several high-level subjects at the University of Oslo, at Institutes of Biology and Informatics, and the Biotechnology Center, as well as at several courses for Education of Medical Doctors in Genetic Toxicology.

Current group leader for two research groups:Development and implementation of high-throughput techniques for genomics: 5 membersClinical bioinformatics: 5 membersAppointed member of the expert group in gene therapy (Senter for medisinsk metodevurdering). Report on gene therapy was published 1999. I am presently member of the steering groups of bioinformatics, gene therapy and microarrays at the Radium Hospital, and appointed leader of the Oslo regional FUGE bioinformatics steering group. I am involved in the creation of an Institutional publication database at the Hospital, and also literature citation tools for the University of Oslo and at the national level.

Industry contacts:I am on the scientific advisory board of the Norwegian Lab-on-a-Chip company NorChip, and I am engaged in the formation of a new company cofunded by Ideas ASA and the Research Foundation, Biomolex AS. This company is based on the patent application of Hovig, Skretting et al. above. Also, I have formed of a Norwegian based bioinformatics company, PubGene Inc., based on the patent application of Jenssen and Hovig above.

Selected publications (last 5 years)

40. Ruud, P., Fodstad, Ø., Hovig, E.: Identification of a novel cytokeratin 19 pseudogene which may interfere with RT-PCR assays used to detect micrometastatic tumor cells. Int J Cancer. Int J Cancer, 80, 119-25, 199941. Wacey, A.I., Cooper, D.N.,Hovig, E., Krawczak, M.: Disentangling the perturbational effects of amino acid substitutions in the DNA-binding domain of p53. Hum Genet, 104, 15-22, 199942. Bjørnland,K., Winberg, J.-O., Ødegaard, O.T., Hovig, E., Loennechen, T., Aasen, A.O., Fodstad, Ø., Mælandsmo, G.M. (1999): S100A4 involvement in metastasis - deragulation of metalloproteinase 2 (MMP-2) and tissue inhibitor of metalloproteinases 2 (TIMP-2)

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activity in osteosarcoma cells transfected with an anti-S100A4 ribozyme. Cancer Res 59(18):4702-843. Dørum, A., Hovig, E., Tropé, C., Inganäs, M., Møller, P. (1999a) 3% of norwegian overain cancers are caused by BRCA1 1675delA or 1135insA., Eur J Cancer 35(5):779-8144. Dørum, A., Heimdal, K., Hovig, E., Inganäs, M., Møller, P. (1999b) Penetrances of BRCA1 1675delA and 1135insA with respect to breast and ovarian cancer., Am J Hum Genet 65(3):671-945. Ree, A.H., Tvermyr, M., Egnebraaten, O., Rooman, M., Røsok, Ø., Hovig, E., Meza-Zapada, L.A., Fodstad, Ø (1999) Expression of a novel factor in breast cancer cells with metastatic potential, Cancer Res 59(18):4675-80.46. Borg, Å., Dørum, A., Heimdal, K., Mæhle, L., Hovig, E., Møller, P. (1999): Two BRCA1 founder mutations, 1135insA and 1675delA, account for one third of Norwegian familial breast-ovarian cancer, and may have lower penetrance than infrequent mutations. Dis Markers 15 (1-3):79-8447. Moller P, Borg A, Heimdal K, Apold J, Vallon-Christersson J, Hovig E, MaehleL. The BRCA1 syndrome and other inherited breast or breast-ovarian cancers in aNorwegian prospective series. Eur J Cancer. 2001 May;37(8):1027-32.48. Hovig E, Maelandsmo G, Mellingsaeter T, Fodstad O, Mielewczyk SS, Wolfe J,Goodchild J. Optimization of hammerhead ribozymes for the cleavage of S100A4 (CAPL) mRNA. Antisense Nucleic Acid Drug Dev. 2001 Apr;11(2):67-75.49. Jenssen TK, Laegreid A, Komorowski J, Hovig E. A literature network of human genes

for high-throughput analysis of gene expression. Nat Genet. 2001 May;28(1):21-8.50. Hovig E, Myklebost O, Aamdal S, Smeland EB. [Gene therapy in cancer]. Tidsskr Nor Laegeforen. 2001 Feb 10;121(4):482-8. Review. Norwegian.51. Ree, A.H., Engebraaten, O., Hovig, E., Fodstad, O. Differential display analysis of breast carcinoma cells enriched by immunomagnetic target cell selection: gene expression profiles in bone marrow target cells. Int J Cancer, 2002. 97(1): p. 28-33.52. Hovig E., Rye P.D., Warren D.J., Nustad K.: CA 125: the end of the beginning.Tumour Biol. 2(6):345-7, 200153. Møller, P., Heimdal, K., Apold, J., Fredriksen, Å., Borg, Å.,Hovig, E., Hagen, A., Hagen, B., Pedersen, J.C., Mæhle, L.: The Norwegian Inherited Breast Cancer Group, The Norwegian Inherited Ovarian Cancer Group: Genetic epidemiology of BRCA1 mutations in Norway. Eur J Cancer. 37(18):2428-34, 200154. Møller P., Borg A., Heimdal K., Apold J., Vallon-Christersson J., Hovig E., MaehleL.: The BRCA1 syndrome and other inherited breast or breast-ovarian cancers in a

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Norwegian prospective series.Eur J Cancer. 37(8):1027-32, 2001.55. Jenssen, T.K., Kuo, W.P., Stokke, T., Hovig, E., Associations between gene expressions in breast cancer and patient survival. Hum Genet, 111(4-5): p. 411-20, 2002.56. Jenssen, T.K., Langaas, M., Kuo, W.P. , Smith-Sorensen, B. , Myklebost, O., E. Hovig, Analysis of repeatability in spotted cDNA microarrays. Nucleic Acids Res, 30(14): p. 3235-44, 2002.57. Wang, J., Nygaard, V., Smith-Sorensen, B., Hovig, E. , Myklebost, O.: MArray: analysing single, replicated or reversed microarray experiments. Bioinformatics, 18(8): p. 1139-40, 2002.58. Jenssen, T.K. and E. Hovig. The semantic web and biology. Drug Discov Today, 7(19): p. 992, 2002.60. Nygaard V, Løland A, Holden M, Langaas M, Rue H, Liu F, Myklebost M, Fodstad Ø, Hovig E, Smith-Sørensen B. Effects of mRNA amplification on gene expression ratios in cDNA experiments estimated by analysis of variance. BMC Genomics. 4(1):11, 2003.59. Andersen, K., Smith-Sørensen, B., Pedersen, K. B., Hovig, E., Myklebost, O., Fodstad, Ø. and Mælandsmo, G. M.: Interferon-g inhibits S100A4 expression independent of apoptosis and cell cycle arrest. Br. J. Cancer, In press.

J. Mini biographies of other key researchers

Anders Løland: b. August 30. 1975. M.Sc., UiO, 1999. Research Scientist, NR. Research interests: Environmental statistics, Bioinformatics, Target tracking, Reliability.

Anders Skrondal: b. July 9, 1961. Ph.D. UiO, 1996. Head of Biostatistics Group, NIPH. Research interests: Generalized linear latent and mixed models, medical statistics, epidemiology, multilevel modeling, longitudinal modeling, structural equation modeling, measurement.

David Hirst: b. February 14 1962. Ph.D., Glasgow, 1988. Senior research scientist, NR. Research interests: Environmental statistics, Marine resources, Classification, Design of experiments, Food sciences, Hierarchical modelling.

Gunnar Brunborg: b. April 10 1947. PhD., UiO, 1996. Senior research scientist, NIPH. Research interests: Male reproductive toxicology, genetic toxicology, DNA repair, microarray expression analysis.

Håkon K. Gjessing: b. October 24 1965. Ph.D., University of Bergen, 1995. Senior Scientist, NIPH. Associate Professor, UiO until 2002. Research interests: Insurance statistics, Medical statistics and epidemiology, Stochastic processes, survival analysis, Statistical methods in genetics.

Ingrid K. Glad: b. June 27 1965. Ph.D., University of Trondheim, 1995. Associate professor, UiO. Research interests: Nonparametric function estimation, Bayesian image analysis, Medical imaging, HIV/AIDS epidemiology, MCMC, Microarray data.

Knut Liestøl: b. August 1. 1949. Ph.D., UiO, 1981. Professor of informatics, UiO. Vice dean, Faculty of Mathematics and Natural Sciences, UiO; member of executive board and chairman of board for basic research, Norwegian Research Council. Research interests: Simulation techniques, decision support and biostatistics with applications in epidemiology and molecular biology.

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Lill Trogstad: b. November 13 1965. MD, UiO 1995. PhD- student, NI PH. Research interests: Perinatal epidemiology, etiological factors in development of preeclampsia; including the impact of maternal and fetal genes and gene-environment interactions.

Lovise Olaug Mæhle: b. 1956. MD PHD. Medical geneticist. Consultant in medical genetics, Section of Genetic counselling, DNR. Research background: Cell biology, DNA technology, genetic epidemiology.

Magne Aldrin: b. April 16. 1959. Ph.D., UiO, 1996. Chief research scientist, NR. Research interests: Ridge regression, Multivariate time series prediction, Statistical modelling of meteorology and vehicle traffic, Marine resources, Health effects of air pollution, Clinical trials, Survey sampling, proteomics.

Magne Thoresen: b. December 9 1966. Ph.D., UiO 2002, Post doc, UiO, Section of Medical Statistics. Research interests: Measurement error in regression models, Generalized Linear Mixed Models.

Marit Holden: b. December 27 1962. Ph.D., UiO, 1988. Senior Research Scientist, NR. Research interest: Theoretical Computer Science, Statistical classification, Image analysis, MCMC, Epidemiology, Bioinformatics, Functional genomics.

Odd O. Aalen: b. May 6 1947. Ph.D. in statistics 1975. Professor in medical statistics UiO from 1985. Leader of the Thematic group Norevent, UiO. Research interests: Survival and event-history analysis.

Ola Haug: b. July 25. 1967. Siv.ing., NTH, 1993. Research scientist, NR. Research interests: Statistics, extreme value statistics, statistical methods in genetics and bioinformatics, finance and insurance.

Ørnulf Borgan: b April 8 1950. Dr. Philos., UiO, 1984. Professor, UiO. Associate editor Annals of Statistics and Scandinavian Journal of Statistics. Research interests: Counting process models, Survival analysis, Case-control methodology.

Per Olaf Ekstrøm: b. 1965. PhD 1997. Postdoc at Center for Environmental Health Sciences, MIT 1997-1999. Clinical research leader at Department of Surgical Oncology, DNR.Research interest: SNP and mutation detection methods.

Per Nafstad: b. Feb 21 1950. MD, MPH, Ph.D, UiO, 1997. Professor in epidemiology, UiO. Senior Researcher, Division of Epidemiology, NIPH. Main research interest: Environmental Epidemiology.

Pål Møller: b. 1946, prof comp 1990. Medical geneticist. Head Section of Genetic Counselling, DNR. Research interests: Haplotyping/intrafamilial associations to describe genetic epidemiology of human diseases; the Norwegian BRCA1 mutations; inherited breast and colorectal cancer; statistical methodology.

Ståle Nygård: b. August 28 1976. Master in statistics 2003, Research-assistant, UiO and Ullevål University Hospital, from 2003. Research interests: Microarray data.

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Sven O. Samuelsen: b. April 10 1957. Ph.D., UiO, 1989. Associate professor, UiO. Adjunct Researcher Department of Epidemiology, NI PH. Research interests: Survival analysis, Design issues in epidemiology.

Thore Egeland: b. February 13 1960. Ph.D., UiO, 1989. Research advisor, Department of Biostatistics, National University Hospital and Adjunct Professor, UiO. Research interests: Medical statistics, Genetics, Forensic Statistics, Geostatistics, Reliability.

Tron A. Moger: b. June 23 1975. Master in statistics 2000. Ph.D.-student, UiO, from 2000. Research interests: Survival analysis, Family studies.

Wenche Nystad: b. March 1952. Dr scient,1999. Senior Research Scientist, Divison of Epidemiology, NI PH. Acting Director, Department of Chronic Diseases. Research interest: Respiratory epidemiology, twin analysis.

K. Selection of publications of named scientist

Aalen, O.O. Frailty models. In: Everitt, B.S.; Dunn, G. (ed.): Statistical Analysis of Medical Data: New Development, Arnold, London. 1998: 59-74 Aalen, O.O.; Borgan, Ø.; Fekjær, H. Covariate adjustment of event histories estimated from Markov chains: The additive approach -- Biometrics. 2001; 57: 993-1001Aalen, O.O.; Gjessing, H.K. Understanding the shape of the hazard rate: a process point of view -- Statistical Science. 2001; 16: 1-22Aalen, O.O.; Hjort, N. L. Frailty models that yield proportional hazards. Statistics and Probability Letters. 2002; 58: 335-342.Aalen, O.O.; Tretli, S. Analyzing incidence of testis cancer by means of a frailty model -- Cancer Causes and Control. 1999; 10 : 285-292Andersen, P.K., Borgan, Ø., Gill, R.D., and Keiding, N. (1993). Statistical models based on counting processes. Springer-Verlag, New York.Bjørheim J, Abrahamsen TW, Kristensen AT, Gaudernack G, Ekstrøm PO.: Approach to analysis of single nucleotide polymorphisms by automated constant denaturant capillary electrophoresis. Mutat Res. 2003 May 15;526(1-2):75-83.Bjørheim J, Gaudernack G, Giercksky KE, Ekstrøm PO.: Direct identification of all oncogenic mutants in KRAS exon 1 by cycling temperature capillary electrophoresis.: Electrophoresis. 2003 Jan;24(1-2):63-9.Borgan, Ø. (2002). Estimation of covariate-dependent Markov transition probabilities from nested case-control data. Statistical Methods in Medical Research 11, 183-202.Borgan, Ø., Goldstein, L., and Langholz, B. (1995). Methods for the analysis of sampled cohort data in the Cox proportional hazards model. Annals of Statistics 23, 1749-1778.Borgan, Ø., Langholz, B., Samuelsen, S.O., Goldstein, L. and Pogoda, J. (2000). Exposure stratified case-cohort designs. Lifetime Data Analysis 6, 39-58.Borgan, Ø.and Langholz, B.(1997). Estimation of excess risk from case-control data using Aalen's linear regression model. Biometrics 53, 690-697.Dupuy BM, Andreassen R, Flones AG, Tomassen K, Egeland T, Brion M, Carracedo A, Olaisen B. Y-chromosome variation in a Norwegian population sample. Forensic Sci Int 2001 Apr 1;117(3):163-173.Egeland T, Mostad PF, Mevåg B, Stenersen M. Beyond traditonal paternity and identification cases: Selecting the most probable pedigree. For. Sci. Int. 2000;110(1):47-59.Egeland T, Mostad PF. Statistical genetics and genetical statistics: a forensic perspective. Scand J Stat, Vol 29, 2002.

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Ekstrøm,PO, Bjørheim,J, Gaudernack,G, and Giercksky,KE.: Population screening of single-nucleotide polymorphisms exemplified by analysis of 8000 alleles. Journal of Biomolecular Screening. 2002 Des;(7):501-506.Eskild, A.; Jonassen, T.Ø.; Heger, B.; Samuelsen, S.O.; Grinde,B.T.O.H.I.V.C.S.G.I.J.N.B. The estimated impact of the CCR-5 d32 gene deletion on HIV disease progression varies with study design. -- AIDS. 1998; 12 :2271-2274Flato B, Smerdel A, Johnston V, Lien G, Dale K, Vinje O, Egeland T, Sorskaar D, Forre O. The influence of patient characteristics, disease variables, and HLA alleles on the development of radiographically evident sacroiliitis in juvenile idiopathic arthritis. Arthritis Rheum 2002 Apr;46(4):986-94Glad, I.K. & Sebastiani, G., 'A Bayesian approach to synthetic magnetic resonance imaging', Biometrika, Vol.82, No.2 1995.Gjessing HK, Aalen OO, Hjort NL. Frailty models based on Lévy processes. Advances in Applied Probability, Vol. 35, pp. 532-550, 2003.Gjessing HK, Skjærven R, Wilcox AJ. Errors in gestational age: Evidence of bleeding early in pregnancy. American Journal of Public Health, Vol. 89, No. 2, pp. 213-218, 1999Kumar, T.; Liestøl, K.; Mæhlen, J.; Hiorth, A.; Jettestuen, E.; Lind, H.; Brorson, S.H. Allele frequencies of apolipoprotein E gene polymorphisms in the protein coding region and promoter region (-491A/T) in a healthy Norwegian population. -- Hum Biol. 2002-02; 74 (1) : 137-142 Langholz, B. and Borgan, Ø. (1997). Estimation of absolute risk from nested case-control data. Biometrics 53, 767-774.Lie RT, Rasmussen S, Brunborg H, Gjessing HK, Lie-Nilsen E, Irgens LM. Fetal and maternal contributions to risk of pre-eclampsia: a population based study. British Medical Journal, Vol. 316, No. 7141, pp. 1343-1347, 1998Liestøl, K.; Andersen, P.K. Updating of covariates and choice of time origin in survival analysis: problems with vaguely defined disease states, Statistics in medicine. 2002; 21 : 3701-14Magnus, P.; Nafstad, P.; Øie, L.; Carlsen, K.; Becher, G.; Kongerud, J.;Carlsen, K.H.; Samuelsen, S.O.; Botten, G.S.; Bakketeig, L.Exposure to nitrogen dioxide and the occurrency of bronchial obstruction in children below 2 years -- International Journal ofepidemiology . 1998; 27 : 995-999Magnus, P., Gjessing, H.K., Skrondal, A. and Skjaerven, R. (2001). The paternal contribution to birth weight. Journal of Epidemiology and Community Health 55, 873-877. Minarik M, Minarikova L, Bjørheim J, Ekstrøm PO.: Cycling gradient capillary electrophoresis: A low-cost tool for high-throughput analysis of genetic variations. Electrophoresis. 2003 Jun;24(11):1716-22.Moger TA, Aalen OO, Heimdal K, Gjessing HK. Analysis of testicular cancer data using a frailty model with familial dependence. Statistics in Medicine, 2003 (in press)Møller P, Borg Å, Evans G, Haites N, Reis, MM, Vasen H, Anderson E, Steel CM, Apold J, Lalloo F, Mæhle L, Gregory H, Heimdal K. Survival in familial breast cancer patients stratified on tumour charactereistics, BRCA mutations and oophorectomy. Int J Cancer 2002; 101: 555-559Møller P, Borg A, Heimdal K, Apold J, Vallon-Christersson J, Hovig E, Mæhle L. The BRCA1 syndrome and other inherited breast or breast-ovarian cancers in a Norwegian prospective series. Eur J Cancer 2001; 37: 1027-1032.Møller P, Evans G, Haites N, Vasen H, Reis MM, Anderson E, Apold J, Hodgson S, Eccles D, Olsson H, Stoppa-Lyonnet D, Chang-Claude J, Morrison PJ, Bevilacqua G, Heimdal K, Mæhle L, Lalloo F, Gregory H, Preece P, Borg Å, Nevin NC, Caligo M, Steel M. Guidelines for follow-up of women at high risk for inherited breast cancer. Consensus

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statement from the Biomed2 Demonstration Programme on Inherited Breast Cancer. Dis Markers 1999; 15, 207-2011.Møller P, Heimdal K, Apold J, Fredriksen Å, Borg Å, Hovig E, Hagen A, Hagen B, Pedersen JC, Mæhle L, The Norwegian Inherited Breast Cancer Group, The Norwegian Inherited Ovarian Cancer Group. Genetic epidemiology of BRCA1 mutations in Norway. Eur J Cancer 2001; 37: 2448-2434.Møller P, Mæhle L, Heimdal K, Dørum A, Apold J, Kaurin RM, Jørgensen G, Helgerud P , Qvist H, Bjørndal H , Kullmann G, Bøhler P , Nysted A, Varhaug JE, Aas T, Fjøsne HE , Due J, Kåresen R, Formoe E, Malme PA, Stedjeberg JO, Svenningsen S, Stenehjem E, Kolnes J, Verhage CCH, Haram S, Rønning GA, Wasmuth H, Hammelbo S, Jacobsen U, Trønnes S, Giercksky KE, Trope C, Kvinnsland S. Prospective findings in breast cancer kindreds. Annual incidence rates according to age, stage at diagnosis, mean sojourn time, and incidence rates for contralateral cancer. The Breast 1998: 7, 55-59.Nygaard, Vigdis; Løland, Anders ; Holden, Marit; Langaas, Mette; Rue, Håvard; Liu, Fang; Myklebost, Ola; Fodstad, Øystein; Hovig, Eivind and Smith-Sørensen, Birgitte Effects of mRNA amplification on gene expression ratios in cDNA experiments estimated by analysis of variance, BMC Genomics Vol. 2003, 4, March 23, 2003. Nystad W, Magnus P, Gulsvik A, Skarpaas IJK, Carlsen KH. Changing prevalence of asthma in school children: evidence for diagnostic changes in asthma in two surveys 13 years apart. Eur Respir J 1997;10:1046-51.Nystad W, Røysamb E, Tambs K, Magnus P, Harris J. Genes and environment in asthma, hay fever and eczema compared to symptoms of the same diseases. Submitted. Nystad W, Samuelsen SO, Nafstad P, Edvardsen E, Stensrud T, Jaakkola JJK. The feasibility of measuring lung function among preschool children. Thorax 2002;57: 1021-7.Nystad W, Skrondal A, Magnus P. Day care attendance, recurrent respiratory tract infections and asthma. Int J Epidemiol 1999;28: 882-7.Opdal SH, Vege A, Egeland T, Musse MA, Rognum TO. Possible role of mtDNA mutations in sudden infant death. Pediatr Neurol 2002 Jul;27(1):23-9.Osnes, K.; Aalen, O.O. Spatial smoothing of cancer survival: a Bayesian approach -- Statistics in Medicine. 1999; 18 : 2087-2099Rabe-Hesketh, S. and Skrondal, A. (2001). Parameterization of multivariate random effects models for categorical data. Biometrics 57 (4), 1256-1264. Rabe-Hesketh, S., Skrondal, A. and Pickles, A. (2004). Generalised multilevel structural equation modelling. Psychometrika. In press.Samuelsen S.O. A pseudolikelihood approach to analysis of nested case-control studies.Biometrika 84, 2, 379-394; 1997.Skrondal, A. (2003). Interaction as departure from additivity in case-control studies: A cautionary note. American Journal of Epidemiology, in press.Skrondal, A. and Rabe-Hesketh, S. (2003). Multilevel logistic regression for polytomous data and rankings. Psychometrika, in press. Skrondal, A. and Rabe-Hesketh, S. (2004). Generalized latent variable modeling: Multilevel, longitudinal and structural equation models. Chapman & Hall/ CRC.Storvik, Geir; Holden, Marit and Bosnes, Vidar, Improving statistical image classification by updating model parameters using unclassified pixels». Proceedings. Industrial Mathematics Week; Norwegian Institute of Technology August 1992.Trogstad LIS, Eskild A, Bruu AL, Jeansson S, Jenum PA. Is preeclampsia an infectious disease? Acta Obstetricia et Gynecologica Scandinavica 2001; 80: 1036-1038.Trogstad LIS, Eskild A, Magnus P, Samuelsen SO, Nesheim BI. Changing paternity and time since last pregnancy; the impact on preeclampsia risk. International Journal of Epidemiology 2001; 30:6, 1317-22.

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Trogstad LIS, Skrondal A, Stoltenberg C, Magnus P, Nesheim BI, Eskild A. Recurrence risk of preeclampsia in twin and singelton pregnancies. Submitted.Zahl, P.H.; Aalen, O.O. Adjusting and comparing survival curves by means of an additive risk model -- Lifetime Data Analysis. 1998; 4 (2) : 149-168

6 Minarik M, Minarikova L, Bjørheim J, Ekstrøm PO.: Cycling gradient capillary electrophoresis: A low-cost tool for high-throughput analysis of genetic variations. Electrophoresis. 2003 Jun;24(11):1716-22. Ekstrøm,PO, Bjørheim,J, Gaudernack,G, and Giercksky,KE.: Population screening of single-nucleotide polymorphisms exemplified by analysis of 8000 alleles. Journal of Biomolecular Screening. 2002 Des;(7):501-506.7 Chesley LC. Hypertensive disorders in pregnancy. New York, NY: Appleton-Century-Crofts, 1978.8 Lie RT, Brunborg H, Gjessing HK, Lie-Nilsen E, Irgens LM. Fetal and maternal contributions to risk of pre-eclampsia: population-based study. BMJ 1998; 316:1343-7. Zusterzeel PLM, Morsche R te, Raijmakers MTM, Roes EM, Peters WHM, Stegers EAP. Paternal contribution to the risk for pre-eclampsia. J Med Genet 2002; 39:44-45.9 Livingston JC, Barton JR, Park V, Haddad B, Phillips O, Sibai B. Maternal and fetal inherited thrombophilias are not related to the development of severe preeclampsia. Am J Obstet Gynecol 2001; 185(1): 153-157. Kim YJ, Williamson RA, Murray JC, Andrews J, Pietscher JJ, Peraud PJ et al. Genetic susceptibility to preeclampsia: Roles of cytosine-to-thimine substitution at nucleotide 677 of the gene for methylenetetrahydrofolate reductase, 68-base pair insertion at nucleotide 844 of the gene for cystathione β-synthase, and factor V Leiden mutation. Am J Obstet Gynecol 2001; 184(6): 1211-1217. Currie L, Peek M, McNiven M Prosser I, Mansour J, Ridgeway J. Is there an increased maternal-infant prevalence of Factor V Leiden in association with severe pre-eclampsia? Br J Obstet Gynaecol 2002; 109: 191-196. Lachmeijer AMA, Arngrimsson R, Bastiaans EJ, Pals G, ten Kate L, de Vries JIP et al. Mutatins in the gene for methylenetetra-hydrofolate reductase, homocysteine levels, and vitamin status in women with a history of preeclampsia. Am J Obstet Gynecol 2001; 184 (3): 394-402. 10 Carousella ED, Dausset J, Rouas-Freiss N. Immunotolerant functions of HLA-G. Cell Mol Life Sci 1999; 55: 327-333.11 Goldman-Wohl DS, Ariel I, Greenfield C, Hochner-Celnikier D, Cross J, Fisher S, Yagel S. Lack of human leukocyte antigen –G expression in extravillous trophoblasts is associated with pre-eclampsia. Mol Hum Reprod 2000; 6: 88-95.12 Deveregne O, Coulomb-L’Herminé A, Capel F, Moussa M, Capron F. Expression of Epstein-Barr Virus-Induced Gene 3, an Interleukin-12 p40-Related Molecule, throughout Human pregnancy. AJP 2001; 1763-76.13 Trogstad LIS, Eskild A, Bruu AL, Jeansson S, Jenum PA. Is preeclampsia an infectious disease? Acta Obstetricia et Gynecologica Scandinavica 2001; 80: 1036-1038.14 Harris JR, Magnus P, Samuelsen SO, Tambs K. No evidence of effects of family environment on asthma. A retrospective study of Norwegian twins. Am J Respir Crit Care Med 1997;156:43-9. 15 Zhang Y et al. Positional cloning of a quantitative trait locus on chromosome 13q14 that influences immunoglobulin E levels and asthma. Nature Genetics May, 2003.17 Møller P, Borg A, Heimdal K, Apold J, Vallon-Christersson J, Hovig E, Mæhle L. The BRCA1 syndrome and other inherited breast or breast-ovarian cancers in a Norwegian prospective series. Eur J Cancer 2001; 37: 1027-32.18 Aalen, O.O.; Tretli, S. Analyzing incidence of testis cancer by means of a frailty model -- Cancer Causes and Control. 1999; 10 : 285-29219 Olsen A.-K., Bjørtuft H., Wiger R., Holme J.A., Seeberg E.C., Bjørås M., Brunborg G. Highly efficient Base Excision Repair (BER) in human and rat male germ cells. Nucleic Acids Res. 2001; 29: 1781-90. 20 Jansen J., Olsen A.-K., Wiger R., Naegeli H., de Boer P., van der Hoeven F., Holme J.A., Brunborg G., Mullenders L. Nucleotide excision repair in rat male germ cells: Low level of repair in intact cells contrasts with high dual incision activity in vitro. Nucleic Acids Res. 2001; 29: 1791-1800.21 Zenzes M.T., Puy L.A., Bielecki R., Reed T. Detection of nezo(a)pyrene diol epoxide-DNA adducts in embryoes from smoking couples: evidence for transmission by spermatozoa. Mol.Hum.Reprod. 1999;125-31.

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22 Olsen A.-K., Duale N., Bjørås M., Larsen C.T., Wiger R., Holme J.A., Seeberg E.C., Brunborg G. Limited repair of 8-hydroxy-7,8-dihydroguanine residues in human testicular cells. Nucleic Acids Res. 2003; 1-13.23 Jansen RC Quantitative trait loci in inbred lines. Handbook of Statistical Genetics, Wiley, 567-597, 200124 Doerge RW, Churchill GA Permutation tests for multiple loci affecting a quantitative character, Genetics, 1996, 142,285-294. Broman KW, Speed TP, A model selection approach for the identification of QTL in experimental corsses, JRSS (B), 2002, 64, 641-656. Kao CH, Zeng ZB, Teasdale RD, Multiple interval mapping for QTL, Genetics, 1999, 152, 1203-1216.25 Shoemaker JS, Painter I and Weir B, Bayesian statistics in genetics. A guide for the uninitiated, Trends in Gnenetics, 1999, 15, 354-358. Maliepaard C, Sillanpää MJ, van Ooijen J, Jansen RC, Arjas E, Bayesian versus frequentists analysis of multiple QTL with an application to an outbred apple cross, Theoretical and Aplied Genetics, 2001.26 George, Mengersen, Davis, Localisation of a QTL via a bayesian approach, Biometrics 56, 2000, 40-51 S. Heath, Genetic linkage analysis using MCMC techniques, in Highly Structured Stochastic Systems, Green, Hjort & Richardson eds., OUP 2003, 363-38127 Sillanpää MJ, Arjas E, Bayesian mapping of QTL from incomplete outbred offspring data, Gnenetics,1999, 151, 1605-1928 Skrondal, A. and Rabe-Hesketh, S. (2004). Generalized latent variable modeling: Multilevel, longitudinal and structural equation models. Chapman & Hall/ CRC.29 Gjessing HK and Lie RT. Case-parent-triad data: Estimating disease gene effects for multiple alleles and SNP haplotypes by extending the log-linear model. Work in progress, 2003.30 Arnoldo Frigessi, Ingrid K. Glad, Marit Holden, Heidi Lyng and Mark van de Wiel, “Towards a Bayesian analysis of cDNA microarray data”, in preparation. 31 See for example van de Vijver M, He YD, van’t Veer L, et al. “A gene expression signature as a predictor of survival in breast cancer” New England J. Medicine, 347, December 2002, 1999-2009 and Rosenwald A, Wright G, Wing CC et al, “ The use of molecular profiling to predict survival after chemiotherapy for diffuse large-B-cell lymphoma”, New England J. Medicine, 346, June 2002, 1937-1947.32 For further criticism see also Tibshirani RJ, Efron B, “ Pre-validation and inference in microarrays”, Stat. Appl. Genetics Mol. Biol., 1,1,1-1833 Aalen, O.O. (1989). A linear regression model for the analysis of life times. Stat. Med., 8, 907-925. 34 Hougaard, P. (2000). Analysis of multivariate survival data. Springer Verlag. Liestøl, K., Andersen, P.K., and Andersen, U. Survival analysis and neural nets. Statistics in Medicine 13, 1189-1200 (1994).35 Li HZ, Luan Y, Kernel Cox regression models for linking gene expression profiles to censored survival data, University of California, Davis, preprint 2003. 36 Lingjærde, O.C. and Liestøl, K. Generalized projection pursuit regression. SIAM Journal of Scientific Computing 20(3), 844-857 (1998). Lingjærde, O.C. and Christophersen, N. Shrinkage structure of partial least squares. Scandinavian Journal of Statistics 27(3), 459-473 (2000).37 GeneSpring38 Ernst Wit, Glasgow University, http://www.stats.gla.ac.uk/~microarray/research.html39 J.R. Pollack, T. Sørlie, C.M. Perou, C.A. Rees, S.S. Jeffrey,P.E. Lønning, R. Tibshirani, D. Botstein, A-L. Børresen-Dale,P.O.Brown (2002). Microarray analysis reveals a major direct role of DNA copy number alteration in the transcriptionalprogram of human breast tumors. Proc. Nat. Acad. Sci. 99 (20),12963-12968.

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