Epistasis Blog

From the Computational Genetics Laboratory at Dartmouth Medical School (www.epistasis.org)

Monday, August 30, 2010

Tests for Compositional Epistasis under Single Interaction-Parameter Models

The following is a nice new paper from VanderWeele on statistical tests for compositional epistasis.

Vanderweele TJ, Laird NM. Tests for Compositional Epistasis under Single
Interaction-Parameter Models. Ann Hum Genet. in press (2010) [PubMed]

Abstract

Summary Compositional epistasis is said to be present when the effect of a genetic factor at one locus is masked by a variant at another locus. Although such compositional epistasis is not equivalent to the presence of an interaction in a statistical model, non-standard tests can sometimes be used to detect compositional epistasis. In this paper we consider empirical tests for compositional epistasis under models for the joint effect of two genetic factors which place no restrictions on the main effects of each factor but constrain the interactive effects of the two factors so as to be captured by a single parameter in the model. We describe the implications of these tests for cohort, case-control, case-only and family-based study designs and we illustrate the methods using an example of gene-gene interaction already reported in the literature.

Thursday, August 19, 2010

The impact of phenocopy on the genetic analysis of complex traits

This is an interesting new paper that shows how phenocopy can significantly impact the power to detect gene-gene interactions. Not really sure why they used the random search option of MDR to do the analysis. Doesn't seem like a very powerful approach. They should have done an exhaustive search.

Lescai F, Franceschi C. The impact of phenocopy on the genetic analysis of complex traits. PLoS One. 2010 Jul 29;5(7):e11876. [PubMed]

Abstract

A consistent debate is ongoing on genome-wide association studies (GWAs). A key point is the capability to identify low-penetrance variations across the human genome. Among the phenomena reducing the power of these analyses, phenocopy level (PE) hampers very seriously the investigation of complex diseases, as well known in neurological disorders, cancer, and likely of primary importance in human ageing. PE seems to be the norm, rather than the exception, especially when considering the role of epigenetics and environmental factors towards phenotype. Despite some attempts, no recognized solution has been proposed, particularly to estimate the effects of phenocopies on the study planning or its analysis design. We present a simulation, where we attempt to define more precisely how phenocopy impacts on different analytical methods under different scenarios. With our approach the critical role of phenocopy emerges, and the more the PE level increases the more the initial difficulty in detecting gene-gene interactions is amplified. In particular, our results show that strong main effects are not hampered by the presence of an increasing amount of phenocopy in the study sample, despite progressively reducing the significance of the association, if the study is sufficiently powered. On the opposite, when purely epistatic effects are simulated, the capability of identifying the association depends on several parameters, such as the strength of the interaction between the polymorphic variants, the penetrance of the polymorphism and the alleles (minor or major) which produce the combined effect and their frequency in the population. We conclude that the neglect of the possible presence of phenocopies in complex traits heavily affects the analysis of their genetic data.

Wednesday, August 11, 2010

Survival dimensionality reduction

This is a nice new paper on using MDR for survival analysis. Free software is available. We also have a paper in revision on a survival MDR method. I will post information on our paper when it is accepted.

Beretta L, Santaniello A, Vanriel PL, Coenen MJ, Scorza R. Survival dimensionality reduction (SDR): development and clinical application of an innovative approach to detect epistasis in presence of right-censored data. BMC Bioinformatics. 2010 Aug 6;11(1):416. [PubMed]

ABSTRACT: BACKGROUND: Epistasis is recognized as a fundamental part of the genetic architecture of individuals. Several computational approaches have been developed to model gene-gene interactions in case-control studies, however, none of them is suitable for time-dependent analysis. Herein we introduce the Survival Dimensionality Reduction (SDR) algorithm, a non-parametric method specifically designed to detect epistasis in lifetime datasets. RESULTS: The algorithm requires neither specification about the underlying survival distribution nor about the underlying interaction model and proved satisfactorily powerful to detect a set of causative genes in synthetic epistatic lifetime datasets with a limited number of samples and high degree of right-censorship (up to 70%). The SDR method was then applied to a series of 386 Dutch patients with active rheumatoid arthritis that were treated with anti-TNF biological agents. Among a set of 39 candidate genes, none of which showed a detectable marginal effect on anti-TNF responses, the SDR algorithm did find that the rs1801274 SNP in the FcgammaRIIa gene and the rs10954213 SNP in the IRF5 gene non-linearly interact to predict clinical remission after anti-TNF biologicals. CONCLUSIONS: Simulation studies and application in a real-world setting support the capability of the SDR algorithm to model epistatic interactions in candidate-genes studies in presence of right-censored data. AVAILABILITY: http://sourceforge.net/projects/sdrproject/

Friday, August 06, 2010

Exploring the genetic basis of variation in gene predictions with a synthetic association study

This is an interesting new paper.

Levin TC, Glazer AM, Pachter L, Brem RB, Eisen MB. Exploring the genetic basis of variation in gene predictions with a synthetic association study. PLoS One. 2010 Jul 29;5(7):e11645. [PubMed]

Abstract

Identifying DNA polymorphisms that affect molecular processes like transcription, splicing, or translation typically requires genotyping and experimentally characterizing tissue from large numbers of individuals, which remains expensive and time consuming. Here we introduce an alternative strategy: a "synthetic association study" in which we computationally predict molecular phenotypes on artificial genomes containing randomly sampled combinations of polymorphic alleles, and perform a classical association study to identify genotypes underlying variation in these computationally predicted annotations. We applied this method to characterize the effects on gene structure of 32,792 single-nucleotide polymorphisms between two strains of the antibiotic producing fungus Penicilium chrysogenum. Although these SNPs represent only 0.1 percent of the nucleotides in the genome, they collectively altered 1.8 percent of predicted gene models between these strains. To determine which SNPs or combinations of SNPs were responsible for this variation, we predicted protein-coding genes in 500 intermediate genomes, each identical except for randomly chosen alleles at each SNP position. Of 30,468 gene models in the genome, 557 varied across these 500 genomes. 226 of these polymorphic gene models (40%) were perfectly correlated with individual SNPs, all of which were within or immediately proximal to the affected gene. The genetic architectures of the other 321 were more complex, with several examples of SNP epistasis that would have been difficult to predict a priori. We expect that many of the SNPs that affect computational gene structure reflect a biologically unrealistic sensitivity of the gene prediction algorithm to sequence changes, and we propose that genome annotation algorithms could be improved by minimizing their sensitivity to natural polymorphisms. However, many of the SNPs we identified are likely to affect transcript structure in vivo, and the synthetic association study approach can be easily generalized to any computed genome annotation to uncover relationships between genotype and important molecular phenotypes.