BOOST: A fast approach to detecting gene-gene interactions in genome-wide case-control studies

Gene-gene interactions have long been recognized to be fundamentally important to understand genetic causes of complex disease traits. At present, identifying gene-gene interactions from genome-wide case-control studies is computationally and methodologically challenging. In this paper, we introduce a simple but powerful method, named `BOolean Operation based Screening and Testing'(BOOST). To discover unknown gene-gene interactions that underlie complex diseases, BOOST allows examining all pairwise interactions in genome-wide case-control studies in a remarkably fast manner. We have carried out interaction analyses on seven data sets from the Wellcome Trust Case Control Consortium (WTCCC). Each analysis took less than 60 hours on a standard 3.0 GHz desktop with 4G memory running Windows XP system. The interaction patterns identified from the type 1 diabetes data set display significant difference from those identified from the rheumatoid arthritis data set, while both data sets share a very similar hit region in the WTCCC report. BOOST has also identified many undiscovered interactions between genes in the major histocompatibility complex (MHC) region in the type 1 diabetes data set. In the coming era of large-scale interaction mapping in genome-wide case-control studies, our method can serve as a computationally and statistically useful tool.Comment: Submitte

Multiple locus linkage analysis of genomewide expression in yeast.

With the ability to measure thousands of related phenotypes from a single biological sample, it is now feasible to genetically dissect systems-level biological phenomena. The genetics of transcriptional regulation and protein abundance are likely to be complex, meaning that genetic variation at multiple loci will influence these phenotypes. Several recent studies have investigated the role of genetic variation in transcription by applying traditional linkage analysis methods to genomewide expression data, where each gene expression level was treated as a quantitative trait and analyzed separately from one another. Here, we develop a new, computationally efficient method for simultaneously mapping multiple gene expression quantitative trait loci that directly uses all of the available data. Information shared across gene expression traits is captured in a way that makes minimal assumptions about the statistical properties of the data. The method produces easy-to-interpret measures of statistical significance for both individual loci and the overall joint significance of multiple loci selected for a given expression trait. We apply the new method to a cross between two strains of the budding yeast Saccharomyces cerevisiae, and estimate that at least 37% of all gene expression traits show two simultaneous linkages, where we have allowed for epistatic interactions. Pairs of jointly linking quantitative trait loci are identified with high confidence for 170 gene expression traits, where it is expected that both loci are true positives for at least 153 traits. In addition, we are able to show that epistatic interactions contribute to gene expression variation for at least 14% of all traits. We compare the proposed approach to an exhaustive two-dimensional scan over all pairs of loci. Surprisingly, we demonstrate that an exhaustive two-dimensional scan is less powerful than the sequential search used here. In addition, we show that a two-dimensional scan does not truly allow one to test for simultaneous linkage, and the statistical significance measured from this existing method cannot be interpreted among many traits

Accurate modeling of confounding variation in eQTL studies leads to a great increase in power to detect trans-regulatory effects

Expression quantitative trait loci (eQTL) studies are an integral tool to investigate the genetic component of gene expression variation. A major challenge in the analysis of such studies are hidden confounding factors, such as unobserved covariates or unknown environmental influences. These factors can induce a pronounced artifactual correlation structure in the expression profiles, which may create spurious false associations or mask real genetic association signals. 

Here, we report PANAMA (Probabilistic ANAlysis of genoMic dAta), a novel probabilistic model to account for confounding factors within an
eQTL analysis. In contrast to previous methods, PANAMA learns hidden factors jointly with the effect of prominent genetic regulators. As a result, PANAMA can more accurately distinguish between true genetic association signals and confounding variation. 

We applied our model and compared it to existing methods on a variety of datasets and biological systems. PANAMA consistently performs better than alternative methods, and finds in particular substantially more trans regulators. Importantly, PANAMA not only identified a greater number of associations, but also yields hits that are biologically more plausible and can be better reproduced between independent studies

The Role of Family-Based Designs in Genome-Wide Association Studies

Genome-Wide Association Studies (GWAS) offer an exciting and promising new research avenue for finding genes for complex diseases. Traditional case-control and cohort studies offer many advantages for such designs. Family-based association designs have long been attractive for their robustness properties, but robustness can mean a loss of power. In this paper we discuss some of the special features of family designs and their relevance in the era of GWAS.Comment: Published in at http://dx.doi.org/10.1214/08-STS280 the Statistical Science (http://www.imstat.org/sts/) by the Institute of Mathematical Statistics (http://www.imstat.org

Improvements and Future Challenges in the Field of Genetically Sensitive Sample Designs

Understanding the sources of individual differences beyond social and economic effects has become a research area of growing interest in psychology, sociology, and economics. A quantitative genetic research design provides the necessary tools for this type of analysis. For a state-of-the-art approach, multigroup data is required. Household panel studies, such as BHPS (Understanding Society) in the UK or the SOEP in Germany, combined with an oversampling of twins, provide a powerful starting point since data from a reasonably large number of non-twin relatives is readily available. In addition to advances in our understanding of genetic and environmental influences on key variables in the social sciences, quantitative genetic analyses of target variables can guide molecular genetic research in the field of employment, earnings, health and satisfaction, as combined twin and sibling or parent data can help overcome serious caveats in molecular genetic research.genetics, twins, psychology, sociology, economics, heritability,environment, multigroup design, BHPS, SOEP

Improvements and Future Challenges in the Field of Genetically Sensitive Sample Designs

Understanding the sources of individual differences beyond social and economic effects has become a research area of growing interest in psychology, sociology, and economics. A quantitative genetic research design provides the necessary tools for this type of analysis. For a state-of-the-art approach, multigroup data is required. Household panel studies, such as BHPS (Understanding Society) in the UK or the SOEP in Germany, combined with an oversampling of twins, provide a powerful starting point since data from a reasonably large number of non-twin relatives is readily available. In addition to advances in our understanding of genetic and environmental influences on key variables in the social sciences, quantitative genetic analyses of target variables can guide molecular genetic research in the field of employment, earnings, health and satisfaction, as combined twin and sibling or parent data can help overcome serious caveats in molecular genetic research.Genetics, twins, psychology, sociology, economics, heritability, environment, multigroup design, BHPS, SOEP