The historic developmental hourglass concept depicts the convergence of animal embryos to a common form during the phylotypic period. Recently, it has been shown that a transcriptomic hourglass is associated with this morphological pattern, consistent with the idea of underlying selective constraints due to intense molecular interactions during body plan establishment. Although plants do not exhibit a morphological hourglass during embryogenesis, a transcriptomic hourglass has nevertheless been identified in the model plant Arabidopsis thaliana. Here, we investigated whether plant hourglass patterns are also found post-embryonically. We found that the two main phase changes during the life cycle of Arabidopsis, from embryonic to vegetative and from vegetative to reproductive development, are associated with transcriptomic hourglass patterns. In contrast, flower development, a process dominated by organ formation, is not. This suggests that plant hourglass patterns are decoupled from organogenesis and body plan establishment. Instead, they may reflect general transitions through organizational checkpoints.

Transposable elements (TEs) are a major source of genome variation across the branches of life. Although TEs may occasionally play an adaptive role in their host’s genome, they are much more often deleterious, and purifying selection is thus an important factor controlling genomic TE loads. In contrast, life history and genomic characteristics such as mating system, parasitism, GC content, and RNAi pathways, have been suggested to account for the startling disparity of TE loads in different species. Previous studies of fungal, plant, and animal genomes have reported conflicting results regarding the direction in which these genomic features drive TE evolution. Many of these studies have had limited power because they studied taxonomically narrow systems, comparing only a limited number of phylogenetically independent contrasts, and did not address long term effects on TE evolution. Here we explicitly test the long term determinants of TE evolution by comparing 42 nematode genomes that span over 500 million years of diversification, and include numerous transitions between life history states and RNAi pathways. We have analysed the reconstructed TE loads of ancestors through the Nematoda phylogeny to account for correlation with GC content and transitions in TE evolutionary models. We also analysed the effect of transitions in life history characteristics and RNAi using ANOVA of phylogenetically independent contrasts. We show that purifying selection against TEs is the dominant force throughout the evolutionary history of Nematoda, as indicated by reconstructed ancestral TE loads, and that strong stochastic Ornstein-Uhlenbeck processes are the underlying models which best explain TE diversification among extant species. In contrast we found no evidence that life history or RNAi variations have a significant influence upon genomic TE load across extended periods of evolutionary history. We suggest that these are largely inconsequential to the large differences in TE content observed between genomes and only by these large-scale comparisons can we distinguish long term and persistent effects from transient effects or misleading random changes.

Speciation often involves the splitting of a lineage and the adaptation of daughter lineages to different environments. It may also involve the merging of divergent lineages, thus creating a stable homoploid hybrid species¹ that constructs a new ecological niche by transgressing ² the ecology of the parental types. Hybrid speciation may also contribute to enigmatic and cryptic biodiversity in the sea.^3,4 The enigmatic walleye pollock, which is not a pollock at all but an Atlantic cod that invaded the Pacific 3.8 Mya,⁵ differs considerably from its presumed closest relatives, the Pacific and Atlantic cod. Among the Atlantic cod, shallow-water coastal and deep-water migratory frontal ecotypes are associated with highly divergent genomic islands;^6,7 however, intermediates remain an enigma.⁸ Here, we performed whole-genome sequencing of over 200 individuals using up to 33 million SNPs based on genotype likelihoods⁹ and showed that the evolutionary status of walleye pollock is a hybrid species: it is a hybrid between Arctic cod and Atlantic cod that transgresses the ecology of its parents. For the first time, we provide decisive evidence that the Atlantic cod coastal and frontal ecotypes are separate species that hybridized, leading to a true-breeding hybrid species that differs ecologically from its parents. We refute monophyly and dichotomous branching of these taxa, and stress the importance of looking beyond branching trees at admixture and hybridity. Our study demonstrates the power of whole-genome sequencing and population genomics in providing deep insights into fundamental processes of speciation. Our study was a starting point for further work aimed at examining the criteria of hybrid speciation,¹⁰ selection, sterility and structural chromosomal variation¹¹ among cod-fish, which are among the most important fish stocks in the world. The hybrid nature of both the walleye pollock and Atlantic cod raises the question concerning the extent to which very profitable fisheries^12,13 depend on hybrid vigour. Our results have implications for management of marine resources in times of rapid climate change.^14,15

Genomic tools allow the study of the whole genome and are facilitating the study of genotype-environment combinations and their relationship with the phenotype. However, most genomic prediction models developed so far are appropriate for Gaussian phenotypes. For this reason, appropriate genomic prediction models are needed for count data, since the conventional regression models used on count data with a large sample size (n) and a small number of parameters (p) cannot be used for genomic-enabled prediction where the number of parameters (p) is larger than the sample size (n). Here we propose a Bayesian mixed negative binomial (BMNB) genomic regression model for counts that takes into account genotype by environment (G × E) interaction. We also provide all the full conditional distributions to implement a Gibbs sampler. We evaluated the proposed model using a simulated data set and a real wheat data set from the International Maize and Wheat Improvement Center (CIMMYT) and collaborators. Results indicate that our BMNB model is a viable alternative for analyzing count data.

We describe a reference panel of 64,976 haplotypes at 39,235,157 SNPs constructed using whole genome sequence data from 20 studies of predominantly European ancestry. Using this resource leads to accurate genotype imputation at minor allele frequencies as low as 0.1%, a large increase in the number of SNPs tested in association studies and can help to refine causal loci. We describe remote server resources that allow researchers to carry out imputation and phasing consistently and efficiently.

Extensive hyperpolymorphism and sequence similarity between the HLA genes make HLA type inference from whole-genome sequencing data a challenging problem. We address these by representing sequences from over 10,000 known alleles in a reference graph structure, enabling accurate read mapping. HLA*PRG, our algorithm, outperforms existing methods by a wide margin and for the first time consistently achieves the accuracy of gold-standard reference methods with one error across 158 alleles tested.

Seed banks are a common characteristics to many plant species, which allow storage of genetic diversity in the soil as dormant seeds for various periods of time. We investigate an above-ground population following a Fisher-Wright model with selection coupled with a deterministic seed bank assuming the length of the seed bank is kept constant and the number of seeds is large. To assess the combined impact of seed banks and selection on genetic diversity, we derive a general diffusion model. We compute the equilibrium solution of the site-frequency spectrum and derive the times to fixation of an allele with and without selection. Finally, it is demonstrated that seed banks enhance the effect of selection onto the site-frequency spectrum while slowing down the time until the mutation-selection equilibrium is reached.

Here we present a method of genome wide inferred study (GWIS) that provides an approximation of genome wide association study (GWAS) summary statistics for a variable that is a function of phenotypes for which GWAS summary statistics, phenotypic means and covariances are available. GWIS can be performed regardless of sample overlap between the GWAS of the phenotypes on which the function depends. As GWIS provides association estimates and their standard errors for each SNP, GWIS can form the basis for polygenic risk scoring, LD score regression, Mendelian randomization studies, biological annotation and other analyses. Here, we replicate a body mass index (BMI) GWAS using GWIS based on a height GWAS and a weight GWAS. We proceed to use a GWIS to further our understanding of the genetic architecture of schizophrenia and bipolar disorder.