Genome-wide Association Studies Aim Research Articles

Interpreting population- and family-based genome-wide association studies in the presence of confounding.

A central aim of genome-wide association studies (GWASs) is to estimate direct genetic effects: the causal effects on an individual's phenotype of the alleles that they carry. However, estimates of direct effects can be subject to genetic and environmental confounding and can also absorb the "indirect" genetic effects of relatives' genotypes. Recently, an important development in controlling for these confounds has been the use of within-family GWASs, which, because of the randomness of mendelian segregation within pedigrees, are often interpreted as producing unbiased estimates of direct effects. Here, we present a general theoretical analysis of the influence of confounding in standard population-based and within-family GWASs. We show that, contrary to common interpretation, family-based estimates of direct effects can be biased by genetic confounding. In humans, such biases will often be small per-locus, but can be compounded when effect-size estimates are used in polygenic scores (PGSs). We illustrate the influence of genetic confounding on population- and family-based estimates of direct effects using models of assortative mating, population stratification, and stabilizing selection on GWAS traits. We further show how family-based estimates of indirect genetic effects, based on comparisons of parentally transmitted and untransmitted alleles, can suffer substantial genetic confounding. We conclude that, while family-based studies have placed GWAS estimation on a more rigorous footing, they carry subtle issues of interpretation that arise from confounding.

A nonparametric test for association with multiple loci in the retrospective case-control study.

The genome-wide association studies aim at identifying common or rare variants associated with common diseases and explaining more heritability. It is well known that common diseases are influenced by multiple single nucleotide polymorphisms (SNPs) that are usually correlated in location or function. In order to powerfully detect association signals, it is highly desirable to take account of correlations or linkage disequilibrium (LD) information among multiple SNPs in testing for association. In this article, we propose a test SLIDE that depicts the difference of the average multi-locus genotypes between cases and controls and derive its variance-covariance matrix in the retrospective design. This matrix is composed of the pairwise LD between SNPs. Thus SLIDE can borrow the strength from an external database in the population of interest with a few thousands to hundreds of thousands individuals to improve the power for detecting association. Extensive simulations show that SLIDE has apparent superiority over the existing methods, especially in the situation involving both common and rare variants, both protective and deleterious variants. Furthermore, the efficiency of the proposed method is demonstrated in the application to the data from the Wellcome Trust Case Control Consortium.

Methods and Tools in Genome-wide Association Studies.

Many traits, such as height, the response to a given drug, or the susceptibility to certain diseases are presumably co-determined by genetics. Especially in the field of medicine, it is of major interest to identify genetic aberrations that alter an individual's risk to develop a certain phenotypic trait. Addressing this question requires the availability of comprehensive, high-quality genetic datasets. The technological advancements and the decreasing cost of genotyping in the last decade led to an increase in such datasets. Parallel to and in line with this technological progress, an analysis framework under the name of genome-wide association studies was developed to properly collect and analyze these data. Genome-wide association studies aim at finding statistical dependencies-or associations-between a trait of interest and point-mutations in the DNA. The statistical models used to detect such associations are diverse, spanning the whole range from the frequentist to the Bayesian setting.Since genetic datasets are inherently high-dimensional, the search for associations poses not only a statistical but also a computational challenge. As a result, a variety of toolboxes and software packages have been developed, each implementing different statistical methods while using various optimizations and mathematical techniques to enhance the computations.This chapter is devoted to the discussion of widely used methods and tools in genome-wide association studies. We present the different statistical models and the assumptions on which they are based, explain peculiarities of the data that have to be accounted for and, most importantly, introduce commonly used tools and software packages for the different tasks in a genome-wide association study, complemented with examples for their application.

Molecular epidemiology and its current clinical use in cancer management

The revelation of the entire human DNA sequence in 2001, and the launching of the international haplotype map (HapMap) project, made the identification of common markers of disease possible, dramatically transforming molecular epidemiology. In recent years, the development of, and discoveries within, human genome research have been rapid, highlighted by the current explosion of genome-wide association studies (GWAS). GWAS aim at finding germline changes that increase cancer risk. An equally important and rapid development had been seen in cancer genomics, with great strides being made in our understanding of somatic mutations that allow and accompany cancer development. In this review we discuss whether it is currently possible to use these new discoveries to aid the reduction of cancer mortality by reducing risk of disease, improving prognosis, and keeping complications due to treatment to a minimum. Findings from GWAS have mostly been used to predict risk, but there is the potential to use them for prognostication and even treatment prediction. Expression arrays have identified prognostic patterns for breast cancer, but few reliable patterns are available for treatment prediction. More importantly, virtually no genetic signatures are available to predict morbidity from treatment. Thus, there is a need to bring different biological techniques together and integrate them with existing clinical oncological care for a simultaneous risk and outcome assessment.

Genomweite Assoziationsstudien in der Hepatologie

Genomewide association studies (GWAS) are being reported for an increasing number of common diseases, including first reports on GWAS for hepatobiliary diseases. Most common liver diseases are multifactorial (complex) diseases that are modified by higher-order interactions between multiple genetic and environmental risk factors. The aim of GWAS is to identify the genetic risk factors contributing to disease susceptibility and/or progression. In GWAS, large patient cohorts are genotyped for genetic markers that cover the whole genome, and genotypes are associated with phenotypes by contingency tests and regression analyses. Recent GWAS have identified "risk genes" for gallstones, fatty liver, primary cholestatic liver diseases and chronic hepatitis C virus (HCV) infection as well as fibrosis progression in HCV-infected patients. For the latter patients, "gene signatures" were developed that are composed of multiple risk variants and are associated with progressive liver fibrosis. Furthermore, mouse models are an important tool to identify novel genetic determinants of complex liver diseases. In large experimental crosses of susceptible and resistant inbred mouse strains, phenotypes are correlated with genome-wide markers by genetic linkage analyses. The findings from genome-wide studies in mice and men may contribute to a better understanding of the pathogenesis of complex liver diseases and provide a framework for the development of "personalised" strategies for prediction, early prevention and therapy.

Cohorts for Heart and Aging Research in Genomic Epidemiology (CHARGE) Consortium: Design of prospective meta-analyses of genome-wide association studies from 5 cohorts.

The primary aim of genome-wide association studies is to identify novel genetic loci associated with interindividual variation in the levels of risk factors, the degree of subclinical disease, or the risk of clinical disease. The requirement for large sample sizes and the importance of replication have served as powerful incentives for scientific collaboration. Methods- The Cohorts for Heart and Aging Research in Genomic Epidemiology Consortium was formed to facilitate genome-wide association studies meta-analyses and replication opportunities among multiple large population-based cohort studies, which collect data in a standardized fashion and represent the preferred method for estimating disease incidence. The design of the Cohorts for Heart and Aging Research in Genomic Epidemiology Consortium includes 5 prospective cohort studies from the United States and Europe: the Age, Gene/Environment Susceptibility-Reykjavik Study, the Atherosclerosis Risk in Communities Study, the Cardiovascular Health Study, the Framingham Heart Study, and the Rotterdam Study. With genome-wide data on a total of about 38 000 individuals, these cohort studies have a large number of health-related phenotypes measured in similar ways. For each harmonized trait, within-cohort genome-wide association study analyses are combined by meta-analysis. A prospective meta-analysis of data from all 5 cohorts, with a properly selected level of genome-wide statistical significance, is a powerful approach to finding genuine phenotypic associations with novel genetic loci. The Cohorts for Heart and Aging Research in Genomic Epidemiology Consortium and collaborating non-member studies or consortia provide an excellent framework for the identification of the genetic determinants of risk factors, subclinical-disease measures, and clinical events.