A recent E-Rare workshop reviewed the ethical aspects of whole exome and whole genome-sequencing studies (WES and WGS, respectively) in rare diseases. Leveraging new genomic technologies, which output vast amounts of known and novel genetic variants, researchers are learning more about the genetic basis and mechanisms involved in rare diseases. In some cases, these findings are translated into diagnostic tools for the benefit of rare disease patients. Among the disclosed data, which can assist in treatment management, incidental findings await, bringing with them ethical concerns for the clinicians, researchers and patients.

The geographic structure of a population, which is modelled as a network of several small random-mating populations or demes exchanging migrants between them, limits the rapid spread of a beneficial allele under strong directional selection to the entire population. This weakens or modifies the hitchhiking effect of the beneficial allele on the pattern of genetic variation at linked neutral loci. Previous studies suggested that the characteristic patterns of polymorphism arise with selective sweeps in such a subdivided population. However, they did not fully address the stochastic pattern, as expected in an actual sample of DNA sequence, of such patterns. This study uses a novel method of individual-based forward-in-time simulation to generate multi-locus neutral polymorphism after a selective sweep in a moderately subdivided population. Population subdivision is shown to cause frequency spectrum to shift slightly such that Tajima's D becomes less negative than expected under a panmictic population. Similarly, the pattern of linkage disequilibrium showed very small change due to population subdivision. On the other hand, the value of Wright's FST at closely linked neutral loci relative to that at unlinked loci greatly increased by population subdivision as predicted by previous studies. Finally, the distribution of the gradient of heterozygosity along the migration path of beneficial mutation, previously suggested to allow the inference of the direction of spread, was investigated. The variance of difference in heterozygosity was much larger than the mean, suggesting that such an inference may not be practical.

In cancer research, high-throughput profiling studies have been extensively conducted, searching for markers associated with prognosis. Owing to the ‘large d, small n’ characteristic, results generated from the analysis of a single dataset can be unsatisfactory. Recent studies have shown that integrative analysis, which simultaneously analyses multiple datasets, can be more effective than single-dataset analysis and classic meta-analysis. In most of existing integrative analysis, the homogeneity model has been assumed, which postulates that different datasets share the same set of markers. Several approaches have been designed to reinforce this assumption. In practice, different datasets may differ in terms of patient selection criteria, profiling techniques, and many other aspects. Such differences may make the homogeneity model too restricted. In this study, we assume the heterogeneity model, under which different datasets are allowed to have different sets of markers. With multiple cancer prognosis datasets, we adopt the accelerated failure time model to describe survival. This model may have the lowest computational cost among popular semiparametric survival models. For marker selection, we adopt a sparse group minimax concave penalty approach. This approach has an intuitive formulation and can be computed using an effective group coordinate descent algorithm. Simulation study shows that it outperforms the existing approaches under both the homogeneity and heterogeneity models. Data analysis further demonstrates the merit of heterogeneity model and proposed approach.

Gene expression profiling of peroxisome-proliferator-activated receptor α (PPARα) has been used in several studies, but there were no consistent results on gene expression patterns involved in PPARα activation in genome-wide due to different sample sizes or platforms. Here, we employed two published microarray datasets both PPARα dependent in mouse liver and applied meta-analysis on them to increase the power of the identification of differentially expressed genes and significantly enriched pathways. As a result, we have improved the concordance in identifying many biological mechanisms involved in PPARα activation. We suggest that our analysis not only leads to more identified genes by combining datasets from different resources together, but also provides some novel hepatic tissue-specific marker genes related to PPARα according to our re-analysis.

In order to formulate the Fundamental Theorem of Natural Selection, Fisher defined the average excess and average effect of a gene substitution. Finding these notions to be somewhat opaque, some authors have recommended reformulating Fisher's ideas in terms of covariance and regression, which are classical concepts of statistics. We argue that Fisher intended his two averages to express a distinction between correlation and causation. On this view, the average effect is a specific weighted average of the actual phenotypic changes that result from physically changing the allelic states of homologous genes. We show that the statistical and causal conceptions of the average effect, perceived as inconsistent by Falconer, can be reconciled if certain relationships between the genotype frequencies and non-additive residuals are conserved. There are certain theory-internal considerations favouring Fisher's original formulation in terms of causality; for example, the frequency-weighted mean of the average effects equaling zero at each locus becomes a derivable consequence rather than an arbitrary constraint. More broadly, Fisher's distinction between correlation and causation is of critical importance to gene-trait mapping studies and the foundations of evolutionary biology.