Epistasis Blog

From the Artificial Intelligence Innovation Lab at Cedars-Sinai Medical Center (www.epistasis.org)

Wednesday, March 31, 2010

On the classification of epistatic interactions

A wonderful new paper on epistasis from Marcus Feldman. I highly recommend reading this one.

Gao H, Granka JM, Feldman MW. On the classification of epistatic interactions. Genetics. 2010 Mar;184(3):827-37. [PubMed]


Modern genomewide association studies are characterized by the problem of "missing heritability." Epistasis, or genetic interaction, has been suggested as a possible explanation for the relatively small contribution of single significant associations to the fraction of variance explained. Of particular concern to investigators of genetic interactions is how to best represent and define epistasis. Previous studies have found that the use of different quantitative definitions for genetic interaction can lead to different conclusions when constructing genetic interaction networks and when addressing evolutionary questions. We suggest that instead, multiple representations of epistasis, or epistatic "subtypes," may be valid within a given system. Selecting among these epistatic subtypes may provide additional insight into the biological and functional relationships among pairs of genes. In this study, we propose maximum-likelihood and model selection methods in a hypothesis-testing framework to choose epistatic subtypes that best represent functional relationships for pairs of genes on the basis of fitness data from both single and double mutants in haploid systems. We gauge the performance of our method with extensive simulations under various interaction scenarios. Our approach performs reasonably well in detecting the most likely epistatic subtype for pairs of genes, as well as in reducing bias when estimating the epistatic parameter (epsilon). We apply our approach to two available data sets from yeast (Saccharomyces cerevisiae) and demonstrate through overlap of our identified epistatic pairs with experimentally verified interactions and functional links that our results are likely of biological significance in understanding interaction mechanisms. We anticipate that our method will improve detection of epistatic interactions and will help to unravel the mysteries of complex biological systems.

Tuesday, March 30, 2010

Replication in genetic studies of complex traits

This is a nice paper from 2004 that I just read for the first time. Even more important now given the frenzy over replication in association studies.

Sillanpää MJ, Auranen K. Replication in genetic studies of complex traits. Ann Hum Genet. 2004 Nov;68(Pt 6):646-57 [PubMed]


Disappointments in replicating initial findings in gene mapping for complex traits are often attributed to small sample sizes and inadequate techniques to determine the threshold value. This is clearly not the whole truth. More fundamental reasons lie in the inherent heterogeneity related to disease, including genetic heterogeneity, differences in allele frequencies, and context-dependency in genetic architecture. There are also other reasons related to the data collection and analysis. Replication may remain a source of frustration unless more emphasis is put on controlling these sources of heterogeneity between studies.

Saturday, March 27, 2010

The discriminative accuracy of genomic profiling in the prediction of common complex diseases

I am enjoying the numerous papers appearing in the literature on the inability of genetic assoication results to accurately predict common complex diseases. The wake up call is here. My advice to current students is to forget everything you have learned over the last five years and go back and read the historical literature from geneticists that think deeply about genetic architecture. I started a reading list last year on Epistasis Blog. See the May 7, 2009 post on 100 papers every graduate student (in genetic epidemiology) should read.

Moonesinghe R, Liu T, Khoury MJ. Evaluation of the discriminative accuracy of genomic profiling in the prediction of common complex diseases. Eur J Hum Genet. 2010 Apr;18(4):485-9. [PubMed]


Genetic testing for susceptibility to common diseases based on a combination of genetic markers may be needed because the effect size associated with each genetic marker is small. Whether or not a genome profile based on a combination of markers could yield a useful test can be evaluated by assessing the discriminative accuracy. The authors present a simple method to calculate the clinical discriminative accuracy of a genomic profile when the relative risk and genotype frequency of each genotype are known. In addition, the clinical discriminative accuracy of a genetic test is presented for given values of the heritability and prevalence of the disease and for the population-attributable fraction of the combined genetic markers. For given values of relative risk and genotype frequency, the discriminative accuracy increases with increasing heritability but declines with increasing prevalence of the disease. For a given value of population-attributable fraction, the discriminative accuracy increases with increasing relative risks, but declines with increasing genotype frequency. On the basis of population-attributable fraction and estimates of heritability of disease, the number of risk genotypes required to have a reasonable clinical discriminative accuracy is much higher than the genome profiles available at present.

Thursday, March 11, 2010

Disease Cause Is Pinpointed With Genome

A recent New York Times article discusses the recent successes with using deep sequencing for rare Mendelian diseases. This is clearly a success. However, those pushing this technology significantly overstate the potential for common human diseases. For example, David Goldstein is quoted as saying “We are finally about to turn the corner, and I suspect that in the next few years human genetics will finally begin to systematically deliver clinically meaningful findings”. Have we not learned the lessons from the past? This the same hype that came with the human genome project, the HapMap project and with GWAS. Technology will not alone solve these problems. We need a fundamental shift in how we think about the compexity of the genetic architecture of common human diseases.