Epistasis Blog

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

Tuesday, July 27, 2010

Hints of hidden heritability in GWAS

This News and Views piece by Greg Gibson summarizes two recent GWAS papers published in Nature Genetics.

Gibson G. Hints of hidden heritability in GWAS. Nat Genet. 2010 Jul;42(7):558-60. [PubMed]

Yang J, Benyamin B, McEvoy BP, Gordon S, Henders AK, Nyholt DR, Madden PA, Heath AC, Martin NG, Montgomery GW, Goddard ME, Visscher PM. Common SNPs explain a large proportion of the heritability for human height. Nat Genet. 2010 Jul;42(7):565-9. [PubMed]

Park JH, Wacholder S, Gail MH, Peters U, Jacobs KB, Chanock SJ, Chatterjee N. Estimation of effect size distribution from genome-wide association studies and implications for future discoveries. Nat Genet. 2010 Jul;42(7):570-5. [PubMed]

Monday, July 26, 2010

Maternal-Zygotic Epistasis and the Evolution of Genetic Diseases

Interesting new paper from Nicholas Priest and Mike Wade.

Priest NK, Wade MJ. Maternal-zygotic epistasis and the evolution of genetic diseases. J Biomed Biotechnol. 2010;2010:478732. [PubMed]


Many birth defects and genetic diseases are expressed in individuals that do not carry the disease causing alleles. Genetic diseases observed in offspring can be caused by gene expression in mothers and by interactions between gene expression in mothers and offspring. It is not clear whether the underlying pattern of gene expression (maternal versus offspring) affects the incidence of genetic disease. Here we develop a 2-locus population genetic model with epistatic interactions between a maternal gene and a zygotic gene to address this question. We show that maternal effect genes that affect disease susceptibility in offspring persist longer and at higher frequencies in a population than offspring genes with the same effects. We find that specific forms of maternal-zygotic epistasis can maintain disease causing alleles at high frequencies over a range of plausible values. Our findings suggest that the strength and form of epistasis and the underlying pattern of gene expression may greatly influence the prevalence of human genetic diseases.

Saturday, July 24, 2010

GWAS: heritability missing in action?

The missing heritability discussion continues in this letter. They do acknowledge gene-gene interactions and cite a few of my papers.

Clarke AJ, Cooper DN. GWAS: heritability missing in action? Eur J Hum Genet. 2010 [PubMed]

"So, where is this ‘missing heritability’? We respond to this question in two different ways. First, we believe that complex disorders are indeed complex and that genetic studies of complex disorders in humans face a number of challenges including gene–gene and gene–environment interactions and epigenetic modification of the genome. Second, we shall argue that high estimates of heritability have been misinterpreted as showing that a predisposition to such a condition (one with high heritability) must have been transmitted through the family from parent to child. The complexity of these common conditions is apparent from the range of factors that need to be considered as potentially contributing to the ‘missing heritability’. These can be rare variants whose significance is not yet recognised, less uncommon variants of small effect, or common variants of very small effect (very weakly penetrant)."

Monday, July 19, 2010

Surfing a genetic association interaction network to identify modulators of antibody response to smallpox vaccine

This is a very nice paper. I like the use of the Google Page-Rank style algorithm.

Davis NA, Crowe JE Jr, Pajewski NM, McKinney BA. Surfing a genetic association interaction network to identify modulators of antibody response to smallpox vaccine. Genes Immun. in press (2010). [PubMed]


The variation in antibody response to vaccination likely involves small contributions of numerous genetic variants, such as single-nucleotide polymorphisms (SNPs), which interact in gene networks and pathways. To accumulate the bits of genetic information relevant to the phenotype that are distributed throughout the interaction network, we develop a network eigenvector centrality algorithm (SNPrank) that is sensitive to the weak main effects, gene-gene interactions and small higher-order interactions through hub effects. Analogous to Google PageRank, we interpret the algorithm as the simulation of a random SNP surfer (RSS) that accumulates bits of information in the network through a dynamic probabilistic Markov chain. The transition matrix for the RSS is based on a data-driven genetic association interaction network (GAIN), the nodes of which are SNPs weighted by the main-effect strength and edges weighted by the gene-gene interaction strength. We apply SNPrank to a GAIN analysis of a candidate-gene association study on human immune response to smallpox vaccine. SNPrank implicates a SNP in the retinoid X receptor alpha (RXRA) gene through a network interaction effect on antibody response. This vitamin A- and D-signaling mediator has been previously implicated in human immune responses, although it would be neglected in a standard analysis because its significance is unremarkable outside the context of its network centrality. This work suggests SNPrank to be a powerful method for identifying network effects in genetic association data and reveals a potential vitamin regulation network association with antibody response.

Saturday, July 17, 2010

Epistasis: A network of interactors

The following is a brief note from Nature Reviews Genetics highlighting several new papers on epistasis. Both look interesting.

Casci T. Epistasis: A network of interactors. Nat Rev Genet. 2010
Aug;11(8):531. [PubMed]

Tuesday, July 13, 2010

Deep Epistasis in Human Metabolism

Interesting new paper. Connecting epistasis with metabolism is very important for understanding how genetic variation impacts complex traits.

Imielinski M, Belta C. Deep epistasis in human metabolism. Chaos. 2010
Jun;20(2):026104. [PubMed]


We extend and apply a method that we have developed for deriving high-order epistatic relationships in large biochemical networks to a published genome-scale model of human metabolism. In our analysis we compute 33,328 reaction sets whose knockout synergistically disables one or more of 43 important metabolic functions. We also design minimal knockouts that remove flux through fumarase, an enzyme that has previously been shown to play an important role in human cancer. Most of these knockout sets employ more than eight mutually buffering reactions, spanning multiple cellular compartments and metabolic subsystems. These reaction sets suggest that human metabolic pathways possess a striking degree of parallelism, inducing "deep" epistasis between diversely annotated genes. Our results prompt specific chemical and genetic perturbation follow-up experiments that could be used to query in vivo pathway redundancy. They also suggest directions for future statistical studies of epistasis in genetic variation data sets.

Monday, July 05, 2010

Is too much data shattering our focus and rewriting our brains?

Wired magazine has a review and essay about a new book called 'The Shallows' by Nicholas Carr. In this book Carr, argues that the internet is rewiring our brains to be good at 'cursory reading, hurried and distracted thinking and superficial learning'. The effect of this this is that very little of what we see on the internet goes in to longterm memory because there isn't time for the brain to make the important connections and establish context. Thus, we don't actually 'learn' very much from the internet. I think the same thing is happening in genetics and epidemiology with the onslaught of data from high-throughput technology. The field is caught up in a perpetual frenzy to adapt to the latest technology being thrown our way. The result is that we spend much of our time in a panic about data cleaning, data management and high-throughput data analysis. We are not spending our valuable time thinking deeply about the questions and the intepretation of research results. Is it possible that the data deluge is resulting in 'cursory reading, hurried and distracted thinking and superficial learning' just as with the internet? What is happening to the students we are training? Are we really training them how to think or are they just learning how to do?