Independent test assessment using the extreme value distribution theory.

The new generation of whole genome sequencing platforms offers great possibilities and challenges for dissecting the genetic basis of complex traits. With a very high number of sequence variants, a naïve multiple hypothesis threshold correction hinders the identification of reliable associations by the overreduction of statistical power. In this report, we examine 2 alternative approaches to improve the statistical power of a whole genome association study to detect reliable genetic associations.

Epigenetics as an answer to Darwin's "special difficulty," Part 2: natural selection of metastable epialleles in honeybee castes.

In a recent perspective in this journal, Herb (2014) discussed how epigenetics is a possible mechanism to circumvent Charles Darwin's "special difficulty" in using natural selection to explain the existence of the sterile-fertile dimorphism in eusocial insects. Darwin's classic book "On the Origin of Species by Means of Natural Selection" explains how natural selection of the fittest individuals in a population can allow a species to adapt to a novel or changing environment. However, in bees and other eusocial insects, such as ants and termites, there exist two or more castes of genetically similar females, from fertile queens to multiple sub-castes of sterile workers, with vastly different phenotypes, lifespans, and behaviors.

Adjusted sequence kernel association test for rare variants controlling for cryptic and family relatedness.

Recent progress in sequencing technologies makes it possible to identify rare and unique variants that may be associated with complex traits. However, the results of such efforts depend crucially on the use of efficient statistical methods and study designs. Although family-based designs might enrich a data set for familial rare disease variants, most existing rare variant association approaches assume independence of all individuals.