The shape of leaves are dynamic, changing over evolutionary time between species, within a single plant producing different shaped leaves at successive nodes, during the development of a single leaf as it allometrically expands, and in response to the environment. Notably, strong correlations between the dissection and size of leaves with temperature and precipitation exist in both the paleorecord and extant populations. Yet, a morphometric model integrating evolutionary, developmental, and environmental effects on leaf shape is lacking. Here, we continue a morphometric analysis of >5,500 leaves representing 270 grapevines of multiple Vitis species between two growing seasons. Leaves are paired one-to-one, vine-to-vine accounting for developmental context, between growing seasons. Linear Discriminant Analysis reveals shape features that specifically define growing season, regardless of species or developmental context. The shape feature, a more pronounced distal sinus, is associated with the colder, drier growing season, consistent with patterns observed in the paleorecord. We discuss the implications of such plasticity in a long-lived woody perennial, such as grapevine, with respect to the evolution and functionality of plant morphology and changes in climate.

The joint and accurate inference of selection and demography from genetic data is considered a particularly challenging question in population genetics, since both process may lead to very similar patterns of genetic diversity. However, additional information for disentangling these effects may be obtained by observing changes in allele frequencies over multiple time points. Such data is common in experimental evolution studies, as well as in the comparison of ancient and contemporary samples. Leveraging this information, however, has been computationally challenging, particularly when considering multi-locus data sets. To overcome these issues, we introduce a novel, discrete approximation for diffusion processes, termed \textit{mean transition time approximation}, which preserves the long-term behavior of the underlying continuous diffusion process. We then derive this approximation for the particular case of inferring selection and demography from time series data under the classic Wright-Fisher model and demonstrate that our approximation is well suited to describe allele trajectories through time, even when only a few states are used. We then develop a Bayesian inference approach to jointly infer the population size and locus-specific selection coefficients with high accuracy, and further extend this model to also infer the rates of sequencing errors and mutations. We finally apply our approach to recent experimental data on the evolution of drug resistance in Influenza virus, identifying likely targets of selection and finding evidence for much larger viral population sizes than previously reported.

Background: The availability of whole-genome sequence data from key ancestors provides an exhaustive catalogue of polymorphic sites segregating within and across cattle breeds. Sequence variants from key ancestors can be imputed in animals that have been genotyped using medium- and high-density genotyping arrays. Association analysis with imputed sequences, particularly if applied to multiple traits simultaneously, is a very powerful approach to revealing candidate causal variants underlying complex phenotypes. Results: We used whole-genome sequence data from 157 key ancestors of the German Fleckvieh population to impute 20 561 798 sequence variants in 10 363 animals that had (partly imputed) array-derived genotypes at 634 109 SNP. The imputed sequence data were enriched for rare variants. Association studies with imputed sequence variants were performed using seven correlated udder conformation traits as response variables. The calculation of an approximate multi-trait test statistic enabled us to detect twelve major QTL (P<2.97 x 10-9) controlling different aspects of mammary gland morphology. Imputed sequence variants were the most significantly associated at eleven QTL, whereas the top association signal at a QTL on BTA14 resulted from an array-derived variant. Seven QTL were associated with multiple phenotypes. Most QTL were located in non-coding regions of the genome in close neighborhood, however, to plausible candidate genes for mammary gland morphology (SP5, GC, NPFFR2, CRIM1, RXFP2, TBX5, RBM19, ADAM12). Conclusions: Association analysis with imputed sequence variants allows QTL characterization at maximum resolution. Multi-trait approaches can reveal QTL that are not detected in single-trait association studies. Most QTL for udder conformation traits were located in non-coding elements of the genome suggesting regulatory mutations to be the major determinants of variation in mammary gland morphology in cattle.

Genotyping-by-sequencing (GBS) and related methods are based on high-throughput short-read sequencing of genomic complexity reductions followed by discovery of SNPs within sequence tags. This provides a powerful and economical approach to whole-genome genotyping, facilitating applications in genomics, diversity analysis, and molecular breeding. However, due to the complexity of analysing large data sets, applications of GBS may require substantial time, expertise and computational resources. Haplotag, the novel GBS software described here, is freely available and operates with minimal user-investment on widely-available computer platforms. Haplotag is unique in fulfilling the following set of criteria: (1) operates without a reference genome; (2) can be used in a polyploid species; (3) provides a discovery mode and a production mode; (4) discovers polymorphisms based on a model of local haplotypes within sequenced tags; (5) reports SNPs as well as haplotype-based genotypes; (6) provides an intuitive visual ???passport??? for each inferred locus.

Metagenome shotgun sequence data offer the potential for large scale biogeographic analysis of microbial species. In this project we developed a method for detecting 33 common subtypes of the pathogenic bacterium Staphylococcus aureus. We used a binomial mixture model implemented in the binstrain software and the coverage counts at > 100,000 known S. aureus SNP (single nucleotide polymorphism) sites derived from prior comparative genomic analysis to estimate the proportion of each subtype in metagenome samples. Using this pipeline we were able to obtain > 87% sensitivity and > 94% specificity when testing on low genome coverage samples of diverse S. aureus strains (0.025X). We found that 321 and 149 metagenome samples from the Human Microbiome Project and metaSUB analysis of the New York City subway, respectively, contained S. aureus at genome coverage > 0.025. In both projects, CC8 and CC30 were the most common S. aureus subtypes encountered. We found evidence that the subtype composition at different body sites of the same individual were more similar than random sampling and more limited evidence that certain body sites were enriched for particular subtypes. One surprising finding was the apparent high frequency of CC398, a lineage associated with livestock, in samples from the tongue dorsum. Epidemiologic analysis of the HMP subject population suggested that high BMI (body mass index) and health insurance are risk factors for S. aureus but there was limited power to find factors linked to carriage of even the most common subtype. In the NYC subway data, we found a small signal of geographic distance affecting subtype clustering but other unknown factors influence taxonomic distribution of the species around the city. We argue that pathogen detection in metagenome samples requires the use of subtypes based on whole species population genomic analysis rather than using ad hoc collections of reference strains.

Summary: Advances in sequencing capacity have lead to the generation of unprecedented amounts of genomic data. The processing of this data frequently leads to I/O bottlenecks, e.g. when analyzing a small genomic region across a large number of samples. The largest I/O burden is, however, often not imposed by the amount of data needed for the analysis but rather by index files that help retrieving this data. We have developed chopBAI, a program that can chop a BAM index (BAI) file into small pieces. The program outputs a list of BAI files each indexing a specified genomic interval. The output files are much smaller in size but maintain compatibility with existing software tools. We show how preprocessing BAI files with chopBAI can lead to a reduction of I/O by more than 95 % during the analysis of 10 Kbp genomic regions, eventually enabling the joint analysis of more than 10,000 individuals. Availability and Implementation: The software is implemented in C++, GPL licensed and available at http://github.com/DecodeGenetics/chopBAI Contact: birte.kehr@decode.is

Whole genome sequencing on next-generation instruments provides an unbiased way to identify the organisms present in complex metagenomic samples. However, the time-to-result can be protracted because of fixed-time sequencing runs and cumbersome bioinformatics workflows. This limits the utility of the approach in settings where rapid species identification is crucial, such as in the quality control of food-chain components, or in during an outbreak of an infectious disease. Here we present What′s in my Pot? (WIMP), a laboratory and analysis workflow in which, starting with an unprocessed sample, sequence data is generated and bacteria, viruses and fungi present in the sample are classified to subspecies and strain level in a quantitative manner, without prior knowledge of the sample composition, in approximately 3.5 hours. This workflow relies on the combination of Oxford Nanopore Technologies′ MinION ™ sensing device with a real-time species identification bioinformatics application.

Shared genetic architecture between phenotypes may be driven by a common genetic basis (pleiotropy) or a subset of genetically similar individuals (heterogeneity). We developed BUHMBOX, a well-powered statistical method to distinguish pleiotropy from heterogeneity using genotype data. We observed a shared genetic basis between 11 of 17 tested autoimmune diseases and type I diabetes (T1D, p<10 12) and 11 of 17 tested autoimmune diseases and rheumatoid arthritis (RA, p<10-7). This sharing could not be explained by heterogeneity (corrected pBUHMBOX>0.2 using 6,670 T1D cases and 7,279 RA cases), suggesting that shared genetic features in autoimmunity are due to pleiotropy. We observed a shared genetic basis between seronegative and seropostive RA (p<10-22), explained by heterogeneity (pBUHMBOX=0.008 in 2,406 seronegative RA cases). Consistent with previous observations, we observed genetic sharing between major depressive disorder (MDD) and schizophrenia (p<10 9). This sharing is not explained by heterogeneity (pBUHMBOX=0.28 in 9,238 MDD cases).

While eukaryotic noncoding RNAs have recently received intense scrutiny, it is becoming clear that bacterial transcription is at least as pervasive. Bacterial small RNAs and antisense RNAs (sRNAs) are often assumed to be noncoding, due to their lack of long open reading frames (ORFs). However, there are numerous examples of sRNAs encoding for small proteins, whether or not they also have a regulatory role at the RNA level. Here, we apply flexible machine learning techniques based on sequence features and comparative genomics to quantify the prevalence of sRNA ORFs under natural selection to maintain protein-coding function in phylogenetically diverse bacteria. A majority of annotated sRNAs have at least one ORF between 10 and 50 amino acids long, and we conservatively predict that 188 ± 25.5 unannotated sRNA ORFs are under selection to maintain coding, an average of 13 per species considered here. This implies that overall at least 7.5 ± 0.3% of sRNAs have a coding ORF, and in some species at least 20% do. 84 ± 9.8 of these novel coding ORFs have some antisense overlap to annotated ORFs. As experimental validation, many of our predictions are translated according to ribosome profiling data and are identified via mass spectrometry shotgun proteomics. B. subtilis sRNAs with coding ORFs are enriched for high expression in biofilms and confluent growth, and two S. pneumoniae sRNAs with coding ORFs are involved in virulence. sRNA coding ORFs are enriched for transmembrane domains and many are novel components of type I toxin/antitoxin systems. Our predictions for sRNA coding ORFs, including novel type I toxins, are freely available in a user-friendly format at http://disco-bac.web.pasteur.fr.