Reconsidering the Heritability of Intelligence in Adulthood: Taking Assortative Mating and Cultural Transmission into Account

Heritability estimates of general intelligence in adulthood generally range from 75 to 85%, with all heritability due to additive genetic influences, while genetic dominance and shared environmental factors are absent, or too small to be detected. These estimates are derived from studies based on the classical twin design and are based on the assumption of random mating. Yet, considerable positive assortative mating has been reported for general intelligence. Unmodeled assortative mating may lead to biased estimates of the relative magnitude of genetic and environmental factors. To investigate the effects of assortative mating on the estimates of the variance components of intelligence, we employed an extended twin-family design. Psychometric IQ data were available for adult monozygotic and dizygotic twins, their siblings, the partners of the twins and siblings, and either the parents or the adult offspring of the twins and siblings (N = 1314). Two underlying processes of assortment were considered: phenotypic assortment and social homogamy. The phenotypic assortment model was slightly preferred over the social homogamy model, suggesting that assortment for intelligence is mostly due to a selection of mates on similarity in intelligence. Under the preferred phenotypic assortment model, the variance of intelligence in adulthood was not only due to non-shared environmental (18%) and additive genetic factors (44%) but also to non-additive genetic factors (27%) and phenotypic assortment (11%).This non-additive nature of genetic influences on intelligence needs to be accommodated in future GWAS studies for intelligence

Transcriptional activity and strain-specific history of mouse pseudogenes

Abstract: Pseudogenes are ideal markers of genome remodelling. In turn, the mouse is an ideal platform for studying them, particularly with the recent availability of strain-sequencing and transcriptional data. Here, combining both manual curation and automatic pipelines, we present a genome-wide annotation of the pseudogenes in the mouse reference genome and 18 inbred mouse strains (available via the mouse.pseudogene.org resource). We also annotate 165 unitary pseudogenes in mouse, and 303, in human. The overall pseudogene repertoire in mouse is similar to that in human in terms of size, biotype distribution, and family composition (e.g. with GAPDH and ribosomal proteins being the largest families). Notable differences arise in the pseudogene age distribution, with multiple retro-transpositional bursts in mouse evolutionary history and only one in human. Furthermore, in each strain about a fifth of all pseudogenes are unique, reflecting strain-specific evolution. Finally, we find that ~15% of the mouse pseudogenes are transcribed, and that highly transcribed parent genes tend to give rise to many processed pseudogenes

Quantitative trait locus analysis in haplodiploid Hymenoptera

This article describes QTL analyses for solitary (Nasonia, a parasitoid wasp) and social hymenopteran species (honeybee and bumblebee). These exemplar QTL analyses determined the genetic basis of morphological, behavioral, and colony level traits. Mapping populations were derived either from lab crosses between highly inbred strains (Nasonia spp.), lab crosses of individuals caught in the field (bumblebees), or offspring from artificially inseminated queens from a managed honeybee population. Using these examples, we demonstrate the importance of a clear understanding of the life history, breeding, and reproductive system of the organism used for a QTL analysis, e.g., haplo-diploidy or reproductive division of labor in social insects. We lead the reader step by step through the process of multiple QTL analyses and describe potential problems and roadblocks (e.g., data handling, statistical, and biological problems) that can obscure or severely impair the results of a QTL study and how to detect and deal with those problems.In particular, we provide a way to empirically estimate the Beavis effect for a larger QTL mapping population and how to estimate a more accurate value of the explained phenotypic variance of each detected QTL using a resampling procedure