GENESTAT: an information portal for design and analysis of genetic association studies.

We present the rationale, the background and the structure for version 2.0 of the GENESTAT information portal (www.genestat.org) for statistical genetics. The fast methodological advances, coupled with a range of standalone software, makes it difficult for expert as well as non-expert users to orientate when designing and analysing their genetic studies. The ultimate ambition of GENESTAT is to guide on statistical methodology related to the broad spectrum of research in genetic epidemiology. GENESTAT 2.0 focuses on genetic association studies. Each entry provides a summary of a topic and gives links to key papers, websites and software. The flexibility of the internet is utilised for cross-referencing and for open editing. This paper gives an overview of GENESTAT and gives short introductions to the current main topics in GENESTAT, with additional entries on the website. Methods and software developers are invited to contribute to the portal, which is powered by a Wikipedia-type engine and allows easy additions and editing.

Testing association in the presence of linkage using the GRE and multiple markers.

It has recently been shown that testing for association in the presence of linkage using a score test based on a gamma random effects (GRE) model is substantially more powerful than using the Family-Based Association Test. A reason for the increased power lies in better specification of the within family correlation structure, induced by linkage. The GRE, as presented in (Jonasdottir et al. 2007 Genet Epidemiol. 31:528-540), only considers one marker at a time and does not readily handle missing parental information. Here we extend the GRE to incorporate information from more than one marker. This extension leads to a haplotype GRE test and also to efficient handling of missing data on parental genotypes. We show that the haplotype GRE, the H-GRE, is substantially more powerful than the haplotype FBAT, the Haplotype-Based-Association Test. We demonstrate the usefulness of the extended GRE, by reanalyzing the collaborative study on the genetics of alcoholism data, allowing for missing parental information.

Multivariate analysis of complex gene expression and clinical phenotypes with genetic marker data.

This paper summarizes contributions to group 12 of the 15th Genetic Analysis Workshop. The papers in this group focused on multivariate methods and applications for the analysis of molecular data including genotypic data as well as gene expression microarray measurements and clinical phenotypes. A range of multivariate techniques have been employed to extract signals from the multi-feature data sets that were provided by the workshop organizers. The methods included data reduction techniques such as principal component analysis and cluster analysis; latent variable models including structural equations and item response modeling; joint multivariate modeling techniques as well as multivariate visualization tools. This summary paper categorizes and discusses individual contributions with regard to multiple classifications of multivariate methods. Given the wide variety in the data considered, the objectives of the analysis and the methods applied, direct comparison of the results of the various papers is difficult. However, the group was able to make many interesting comparisons and parallels between the various approaches. In summary, there was a consensus among authors in group 12 that the genetic research community should continue to draw experiences from other fields such as statistics, econometrics, chemometrics, computer science and linear systems theory.

HLA-A confers an HLA-DRB1 independent influence on the risk of multiple sclerosis.

A recent high-density linkage screen confirmed that the HLA complex contains the strongest genetic factor for the risk of multiple sclerosis (MS). In parallel, a linkage disequilibrium analysis using 650 single nucleotide polymorphisms (SNP) markers of the HLA complex mapped the entire genetic effect to the HLA-DR-DQ subregion, reflected by the well-established risk haplotype HLA-DRB1*15,DQB1*06. Contrary to this, in a cohort of 1,084 MS patients and 1,347 controls, we show that the HLA-A gene confers an HLA-DRB1 independent influence on the risk of MS (P = 8.4x10(-10)). This supports the opposing view, that genes in the HLA class I region indeed exert an additional influence on the risk of MS, and confirms that the class I allele HLA-A*02 is negatively associated with the risk of MS (OR = 0.63, P = 7x10(-12)) not explained by linkage disequilibrium with class II. The combination of HLA-A and HLA-DRB1 alleles, as represented by HLA-A*02 and HLA-DRB1*15, was found to influence the risk of MS 23-fold. These findings imply complex autoimmune mechanisms involving both the regulatory and the effector arms of the immune system in the triggering of MS.

Testing association in the presence of linkage--a powerful score for binary traits.

We present a score for testing association in the presence of linkage for binary traits. The score is robust to varying degrees of linkage, and it is valid under any ascertainment scheme based on trait values as well as under population stratification. The score test is derived from a mixed effects model where population level association is modeled using a fixed effect and where correlation among related individuals is allowed for by using log-gamma random effects. The score, as presented in this paper, does not assume full information about the inheritance pattern in families or parental genotypes. We compare the score to the semi-parametric family-based association test (FBAT), which has won ground because of its flexible and simple form. We show that a random effects formulation of co-inheritance can improve the power substantially. We apply the method to data from the Collaborative Study on the Genetics of Alcoholism. We compare our findings to previously published results.

CYP17 gene polymorphism in relation to breast cancer risk: a case-control study.

INTRODUCTION: The c.1-34T>C 5' promoter region polymorphism in cytochrome P450c17 (CYP17), a key enzyme in the biosynthesis of estrogen, has been associated with breast cancer risk, but most previous studies have been relatively small. METHODS: We genotyped 1,544 incident cases of primary breast cancer and 1,502 population controls, all postmenopausal Swedish women, for the CYP17 c.1-34T>C polymorphism and calculated odds ratios (ORs) and 95% confidence intervals (CIs) from logistic regression models. RESULTS: No overall association was found between CYP17 c.1-34T>C and breast cancer risk, OR 1.0 (95% CI 0.8-1.3) for the A2/A2 (CC) carriers compared to the A1/A1 (TT) carriers, regardless of histopathology. We detected an interaction between CYP17 c.1-34T>C and age at menarche (P = 0.026) but regarded that as a chance finding as no dose-response pattern was evident. Other breast cancer risk factors, including menopausal hormone use and diabetes mellitus, did not modify the overall results. CONCLUSION: It is unlikely that CYP17 c.1-34T>C has a role in breast cancer etiology, overall or in combination with established non-genetic breast cancer risk factors.

Analysis of binary traits: testing association in the presence of linkage.

Most methods for testing association in the presence of linkage, using family-based studies, have been developed for continuous traits. FBAT (family-based association tests) is one of few methods appropriate for discrete outcomes. In this article we describe a new test of association in the presence of linkage for binary traits. We use a gamma random effects model in which association and linkage are modelled as fixed effects and random effects, respectively. We have compared the gamma random effects model to an FBAT and a generalized estimating equation-based alternative, using two regions in the Genetic Analysis Workshop 14 simulated data. One of these regions contained haplotypes associated with disease, and the other did not.

Cytochrome P450 1B1 gene polymorphisms and postmenopausal endometrial cancer risk.

Estrogen unopposed by progestins is a key factor in endometrial cancer etiology. Cytochrome P450 1B1 (CYP1B1), responsible for the 4-hydroxylation of estrogen, may be important in endometrial carcinogenesis, either as a regulator of estrogen availability or as a producer of potentially genotoxic estrogen metabolites. We investigated the association of CYP1B1 genotype and endometrial cancer risk in a population-based case-control study of postmenopausal Swedish women. We used the Expectation-Maximization algorithm to estimate the haplotype frequencies in the population and calculated odds ratios and 95% confidence intervals from conditional logistic regression models. In stratified analysis, we investigated the possible effects of CYP1B1 genotype on endometrial cancer risk in subgroups defined primarily by menopausal hormone use and also by body mass index, smoking, use of combined oral contraceptives, and family history. We genotyped 689 cases and 1,549 controls for the CYP1B1 single nucleotide polymorphisms m2, m3, and m4 and estimated the haplotype frequencies among controls to 0.086, 0.291, 0.452, and 0.169 for the CYP1B1*1, CYP1B1*2, CYP1B1*3, and CYP1B1*4 alleles, respectively. We found no evidence for an overall association between CYP1B1 genotype and endometrial cancer risk, nor was there any clear indication of gene-environment interaction.