Reticulate evolution is thought to accelerate the process of evolution beyond simple genetic drift and selection, helping to rapidly generate novel hybrids with combinations of adaptive traits. However, the long-standing dogma that reticulate evolutionary processes are likewise advantageous for switching ecological niches, as in microbial pathogen host switch events, has not been explicitly tested. We use data from the influenza genome sequencing project and a phylogenetic heuristic approach to show that reassortment, a reticulate evolutionary mechanism, predominates over mutational drift in transmission between different host species. Moreover, as host evolutionary distance increases, reassortment is increasingly favored. We conclude that the greater the quantitative difference between ecological niches, the greater the importance of reticulate evolutionary processes in overcoming niche barriers.

Adaptation in diploids is predicted to proceed via mutations that are at least partially dominant in fitness. Recently we argued that many adaptive mutations might also be commonly overdominant in fitness. Natural (directional) selection acting on overdominant mutations should drive them into the population but then, instead of bringing them to fixation, should maintain them as balanced polymorphisms via heterozygote advantage. If true, this would make adaptive evolution in sexual diploids differ drastically from that of haploids. Unfortunately, the validity of this prediction has not yet been tested experimentally. Here we performed 4 replicate evolutionary experiments with diploid yeast populations (Saccharomyces cerevisiae) growing in glucose-limited continuous cultures. We sequenced 24 evolved clones and identified initial adaptive mutations in all four chemostats. The first adaptive mutations in all four chemostats were three CNVs, all of which proved to be overdominant in fitness. The fact that fitness overdominant mutations were always the first step in independent adaptive walks strongly supports the prediction that heterozygote advantage can arise as a common outcome of directional selection in diploids and demonstrates that overdominance of de novo adaptive mutations in diploids is not rare.

Lamarckian inheritance has been largely discredited until the recent discovery of transgenerational epigenetic inheritance. However, transgenerational epigenetic inheritance is still under debate for unable to rule out DNA sequence changes as the underlying cause for heritability. Here, through profiling of the sperm methylomes and genomes of two recently diverged rat subspecies, we analyzed the relationship between epigenetic variation and DNA variation, and their relative contribution to evolution of species. We found that only epigenetic markers located in differentially methylated regions (DMRs) between subspecies, but not within subspecies, can be stably and effectively passed through generations. DMRs in response to both random and stable environmental difference show increased nucleotide diversity, and we demonstrated that it is variance of methylation level but not deamination caused by methylation driving increasing of nucleotide diversity in DMRs, indicating strong relationship between environment-associated changes of chromatin accessibility and increased nucleotide diversity. Further, we detected that accelerated fixation of DNA variants occur only in inter-subspecies DMRs in response to stable environmental difference but not intra-subspecies DMRs in response to random environmental difference or non-DMRs, indicating that this process is possibly driven by environment-associated fixation of divergent methylation status. Our results thus establish a bridge between Lamarckian inheritance and Darwinian selection.

Measurements from structural brain magnetic resonance imaging (MRI) scans have been increasingly analyzed as intermediate phenotypes to bridge the gap between clinical features and genetic variation. To date, most imaging phenotypes are scalar, such as the volume of a brain region, which can miss subtle or localized morphological variation associated with genetics or relevant to disease. Neuroanatomical shape measurements – multidimensional geometric descriptions of a brain structure – provide an alternate class of phenotypes that remain largely unexplored. In this paper, we extend the concept of heritability to multidimensional traits, and present the first comprehensive analysis of the heritability of neuroanatomical shape measurements across an ensemble of brain structures based on genome-wide single nucleotide polymorphism (SNP) and MRI data from 1,317 unrelated, young (18-35 years) and healthy individuals. Our results demonstrate that neuroanatomical shape can be significantly heritable, above and beyond volume, and thus can serve as a complementary phenotype to study the genetic underpinnings and clinical relevance of brain structure.

Genome-wide association studies (GWAS) promised to translate their findings into clinically beneficial improvements of patient management by tailoring disease management to the individual through the prediction of disease risk. However, the ability to translate genetic findings from GWAS into predictive tools that are of clinical utility and which may inform clinical practice has, so far, been encouraging but limited. Here we propose to use a more powerful statistical approach that enables the prediction of multiple medically relevant phenotypes without the costs associated with developing a genetic test for each of them. As a proof of principle, we used a common panel of 319,038 SNPs to train the prediction models in 114,264 unrelated White-British for height and four obesity related traits (body mass index, basal metabolic rate, body fat percentage, and waist-to-hip ratio). We obtained prediction accuracies that ranged between 46% and 75% of the maximum achievable given their explained heritable component. This represents an improvement of up to 75% over the phenotypic variance explained by the predictors developed through large collaborations, which used more than twice as many training samples. Across-population predictions in White non-British individuals were similar to those of White-British whilst those in Asian and Black individuals were informative but less accurate. The genotyping of circa 500,000 UK Biobank participants will yield predictions ranging between 66% and 83% of the maximum. We anticipate that our models and a common panel of genetic markers, which can be used across multiple traits and diseases, will be the starting point to tailor disease management to the individual. Ultimately, we will be able to capitalise on whole-genome sequence and environmental risk factors to realise the full potential of genomic medicine.

Inferring and understanding changes in effective population size over time is a major challenge for population genetics. Here we investigate some theoretical properties of random mating populations with varying size over time. In particular, we present an exact method to compute the population size as a function of time using the distributions of coalescent-times of samples of any size. This result reduces the problem of population size inference to a problem of estimating coalescent-time distributions. Using tree inference algorithms and genetic data, we can investigate the effects of a range of conditions associated with real data, for instance finite number of loci, sample size, mutation rate and presence of cryptic recombination. We show that our method requires at least a modest number of loci (10,000 or more) and that increasing the sample size from 2 to 10 greatly improves the inference whereas further increase in sample size only results in a modest improvement, even under as scenario of exponential growth. We also show that small amounts of recombination can lead to biased population size reconstruction when unaccounted for. The approach can handle large sample sizes and the computations are fast. We apply our method on human genomes from 4 populations and reconstruct population size profiles that are coherent with previous knowledge, including the Out-of-Africa bottleneck. Additionally, a potential difference in population size between African and non-African populations as early as 400 thousand years ago is uncovered.

Understanding the driving forces and molecular processes underlying dioecy and sex chromosome evolution, leading from hermaphroditism to the occurrence of male and female individuals, is of considerable interest in fundamental and applied research. The genus Phoenix, belonging to the family Arecaceae, consists of only dioecious species. Phylogenetic data suggests that the genus Phoenix diverged from a hermaphroditic ancestor shared with its closest relatives. Here we investigated the evolution of suppressed recombination within the genus Phoenix as a whole by extending the analysis of P. dactylifera sex-related loci to eight other species within the genus. We also performed a phylogenetic analysis of a date palm sex-linked PdMYB1 gene in these species. We found that X and Y sex-linked alleles clustered in a species-independent fashion. Our data show that sex chromosomes evolved before the diversification of the extant dioecious species. Furthermore, the distribution of Y haplotypes revealed two male ancestral paternal lineages which may have emerged prior to speciation.

Tardigrades are meiofaunal ecdysozoans and are key to understanding the origins of Arthropoda. We present the genome of the tardigrade Hypsibius dujardini, assembled from Illumina paired and mate-pair data. While the raw data indicated extensive contamination with bacteria, presumably from the gut or surface of the animals, careful cleaning generated a clean tardigrade dataset for assembly. We also generated an expressed sequence tag dataset, a Sanger genome survey dataset and used these and Illumina RNA-Seq data for assembly validation and gene prediction. The genome assembly is ~130 Mb in span, has an N50 length of over 50 kb, and an N90 length of 6 kb. We predict 23,031 protein-coding genes in the genome, which is available in a dedicated genome browser at http://www.tardigrades.org. We compare our assembly to a recently published one for the same species and do not find support for massive horizontal gene transfer. Additional analyses of the genome are ongoing.

Most disease associated genetic risk factors are non-coding, making it challenging to design experiments to understand their functional consequences1,2. Identification of expression quantitative trait loci (eQTLs) has been a powerful approach to infer downstream effects of disease variants but the large majority remains unexplained.3,4. The analysis of DNA methylation, a key component of the epigenome5, offers highly complementary data on the regulatory potential of genomic regions6,7. However, a large-scale, combined analysis of methylome and transcriptome data to infer downstream effects of disease variants is lacking. Here, we show that disease variants have wide-spread effects on DNA methylation in trans that likely reflect the downstream effects on binding sites of cis-regulated transcription factors. Using data on 3,841 Dutch samples, we detected 272,037 independent cis-meQTLs (FDR < 0.05) and identified 1,907 trait-associated SNPs that affect methylation levels of 10,141 different CpG sites in trans (FDR < 0.05), an eight-fold increase in the number downstream effects that was known from trans-eQTL studies3,8,9. Trans-meQTL CpG sites are enriched for active regulatory regions, being correlated with gene expression and overlap with Hi-C determined interchromosomal contacts10,11. We detected many trans-meQTL SNPs that affect expression levels of nearby transcription factors (including NFKB1, CTCF and NKX2-3), while the corresponding trans-meQTL CpG sites frequently coincide with its respective binding site. Trans-meQTL mapping therefore provides a strategy for identifying and better understanding downstream functional effects of many disease-associated variants.