Ten years of genetics and genomics: what have we achieved and where are we heading?

To celebrate the first 10 years of Nature Reviews Genetics, we asked eight leading researchers for their views on the key developments in genetics and genomics in the past decade and the prospects for the future. Their responses highlight the incredible changes that the field has seen, from the explosion of genomic data and the many possibilities it has opened up to the ability to reprogramme adult cells to pluripotency. The way ahead looks similarly exciting as we address questions such as how cells function as systems and how complex interactions among genetics, epigenetics and the environment combine to shape phenotypes

Genetic interactions affecting human gene expression identified by variance association mapping

Non-additive interaction between genetic variants, or epistasis, is a possible explanation for the gap between heritability of complex traits and the variation explained by identified genetic loci. Interactions give rise to genotype dependent variance, and therefore the identification of variance quantitative trait loci can be an intermediate step to discover both epistasis and gene by environment effects (GxE). Using RNA-sequence data from lymphoblastoid cell lines (LCLs) from the TwinsUK cohort, we identify a candidate set of 508 variance associated SNPs. Exploiting the twin design we show that GxE plays a role in ∼70% of these associations. Further investigation of these loci reveals 57 epistatic interactions that replicated in a smaller dataset, explaining on average 4.3% of phenotypic variance. In 24 cases, more variance is explained by the interaction than their additive contributions. Using molecular phenotypes in this way may provide a route to uncovering genetic interactions underlying more complex traits.DOI: http://dx.doi.org/10.7554/eLife.01381.001

Teaching for Epidemiological Literacy: Description, Prescription, and Critical Thinking

This working paper describes contrasting ideas for a sequence of topics as presented to students in a graduate course on epidemiological literacy. The premise of the pedagogical approach is that researchers develop their epidemiological thinking and practice over time through interactions with other researchers who have a variety of in-practice commitments, such as to kinds of cases and methods of analysis, and not simply to a philosophical framework for explanation. In descriptively teasing out what epidemiologists do in practice through a topic-by-topic presentation, I am prescriptively encouraging discussants to draw purposefully from across the range of topics and contrasting positions, and thereby pursue critical thinking in the sense of understanding ideas and practices better when we examine them in relation to alternatives. The initial topic concerns ways to learn in a community; after that, a number of conceptual steps follow—the characterization of the very phenomena we might be concerned with, the scope and challenges of the field of epidemiology, the formulation of categories—before linking associations, predictions, causes and interventions and examining the confounding of purported links. Building on that basis, the remaining topics consist of issues or angles of analysis related to the complexities of inequalities within and between populations, context, and changes over the life course. In the course of the description, some assertions about explanation and intervention emerge, notably, that epidemiological-philosophical discussion about causality often leaves unclear or unexamined whether a modifiable factor shown to have been associated with a difference in the data from past observations should be thought of as factor that, when modified, would generate that difference going forward. The article ends with conjectures that concern heterogeneity and the agency of the subjects of epidemiology

Discovering Higher-order SNP Interactions in High-dimensional Genomic Data

In this thesis, a multifactor dimensionality reduction based method on associative classification is employed to identify higher-order SNP interactions for enhancing the understanding of the genetic architecture of complex diseases. Further, this thesis explored the application of deep learning techniques by providing new clues into the interaction analysis. The performance of the deep learning method is maximized by unifying deep neural networks with a random forest for achieving reliable interactions in the presence of noise