Genetic studies of Autism Spectrum Disorder (ASD) have established that de novo duplications and deletions contribute to risk. However, ascertainment of structural variation (SV) has been restricted by the coarse resolution of current approaches. By applying a custom pipeline for SV discovery, genotyping and de novo assembly to genome sequencing of 235 subjects, 71 cases, 26 sibling controls and their parents, we present an atlas of 1.2 million SVs (5,213/genome), comprising 11 different classes. We demonstrate a high diversity of de novo mutations, a majority of which were undetectable by previous methods. In addition, we observe complex mutation clusters where combinations of de novo SVs, nucleotide substitutions and indels occurred as a single event. We estimate a high rate of structural mutation in humans (20%). Genetic risk for ASD is attributable to an elevated frequency of gene-disrupting de novo SVs but not an elevated rate of genome rearrangement.

Demographic, genetic, or stochastic factors can lead to perfect linkage disequilibrium (LD) between alleles at two loci without respect to the extent of their physical distance, a phenomenon that Lawrence et al. (2005a) refer to as “genetic indistinguishability”. This phenomenon can complicate genotype-phenotype association testing by hindering the ability to localize causal alleles, but has not been thoroughly explored from a theoretical perspective or using large, dense whole-genome polymorphism datasets. We derive a simple theoretical model of the prevalence of genetic indistinguishability between unlinked loci, and verify its accuracy via simulation. We show that sample size and minor allele frequency are the major determinants of the prevalence of perfect LD between unlinked loci but that demographic factors, such as deviations from random mating, can produce significant effects as well. Finally, we quantify this phenomenon in three model organisms and find thousands of pairs of moderate-frequency (>5%) genetically indistinguishable variants in relatively large datasets. These results clarify a previously underexplored population genetic phenomenon with important implications for association studies, and define conditions under which it is likely to manifest.

Large-scale reference data sets of human genetic variation are critical for the medical and functional interpretation of DNA sequence changes. Here we describe the aggregation and analysis of high-quality exome (protein-coding region) sequence data for 60,706 individuals of diverse ethnicities. The resulting catalogue of human genetic diversity has unprecedented resolution, with an average of one variant every eight bases of coding sequence and the presence of widespread mutational recurrence. The deep catalogue of variation provided by the Exome Aggregation Consortium (ExAC) can be used to calculate objective metrics of pathogenicity for sequence variants, and to identify genes subject to strong selection against various classes of mutation; we identify 3,230 genes with near-complete depletion of truncating variants, 79% of which have no currently established human disease phenotype. Finally, we show that these data can be used for the efficient filtering of candidate disease-causing variants, and for the discovery of human knockout variants in protein-coding genes.

One important outcome of biological introductions is to bring into contact species that diverged in allopatry. For interfertile taxa, the evolutionary outcomes of such secondary contacts may be diverse (e.g. adaptive introgression from or into the introduced species) but are not yet well examined in the wild. In this context, the recent secondary contact between the non-native species Ciona robusta and the native species C.intestinalis, in the English Channel, provides an excellent case study to examine. By means of a population genomic approach, using 310 SNPs developed from full transcriptomes, the genetic diversity at population and species level was examined by studying 449 individuals (NC.robusta = 213, NC. intestinalis = 236) sampled in 12 sites from the English Channel, North Sea, NW Atlantic and SE Pacific where they are found either alone or living in the same locality and habitat (syntopy). As expected from previous analyses, C. robusta showed less polymorphism than C. intestinalis, a pattern that may partly be explained by its non-native status in most of the study localities. The results clearly showed an almost complete absence of contemporary gene flow between the two species in syntopic localities, with only one first generation hybrid and none other genotype compatible with recent backcrosses. Interestingly, introgression was also observed in allopatric populations of both species (i.e. where no contemporary hybridization can occur). Furthermore, one allopatric population sampled in SE Pacific exhibited a much higher introgression rate compared to all others C. robusta populations. Altogether, these results indicate that the observed inter-specific gene flow is the outcome of historical introgression, spread afterward at a worldwide scale. They also point out that efficient barriers are preventing hybridization in the wild between the introduced and native species in the English Channel, thus making adaptive introgression of the introduced species unlikely to favor the sustainable establishment of the study non-native species.

Given a massive collection of sequences, it is infeasible to perform pairwise alignment for basic tasks like sequence clustering and search. To address this problem, we demonstrate that the MinHash technique, first applied to clustering web pages, can be applied to biological sequences with similar effect, and extend this idea to include biologically relevant distance and significance measures. Our new tool, Mash, uses MinHash locality-sensitive hashing to reduce large sequences to a representative sketch and rapidly estimate pairwise distances between genomes or metagenomes. Using Mash, we explored several use cases, including a 5,000-fold size reduction and clustering of all ~55,000 NCBI RefSeq genomes in 46 CPU hours. The resulting 93 MB sketch database includes all RefSeq genomes, effectively delineates known species boundaries, reconstructs approximate phylogenies, and can be searched in seconds using assembled genomes or raw sequencing runs from Illumina, Pacific Biosciences, and Oxford Nanopore. For metagenomics, Mash scales to thousands of samples and can replicate Human Microbiome Project and Global Ocean Survey results in a fraction of the time. Other potential applications include any problem where an approximate, global sequence distance is acceptable, e.g. to triage and cluster sequence data, assign species labels to unknown genomes, quickly identify mis-tracked samples, and search massive genomic databases. In addition, the Mash distance metric is based on simple set intersections, which are compatible with homomorphic encryption schemes. To facilitate integration with other software, Mash is implemented as a lightweight C++ toolkit and freely released under a BSD license at https://github.com/marbl/mash.

Recent advancements in sequencing technology have led to a drastic reduction in the cost of genome sequencing. This development has generated an unprecedented amount of genomic data that must be stored, processed, and communicated. To facilitate this effort, compression of genomic files has been proposed. Specifically, lossy compression of quality scores is emerging as a natural candidate for reducing the growing costs of storage. A main goal of performing DNA sequencing in population studies and clinical settings is to identify genetic variation. Though the field agrees that smaller files are advantageous, the cost of lossy compression, in terms of variant discovery, is unclear. Bioinformatic algorithms to identify SNPs and INDELs from next-generation DNA sequencing data use base quality score information; here, we evaluate the effect of lossy compression of quality scores on SNP and INDEL detection. We analyze several lossy compressors introduced recently in the literature. Specifically, we investigate how the output of the variant caller when using the original data (uncompressed) differs from that obtained when quality scores are replaced by those generated by a lossy compressor. Using gold standard genomic datasets such as the GIAB (Genome In A Bottle) consensus sequence for NA12878 and simulated data, we are able to analyze how accurate the output of the variant calling is, both for the original data and that previously lossily compressed. We show that lossy compression can significantly alleviate the storage while maintaining variant calling performance comparable to that with the uncompressed data. Further, in some cases lossy compression can lead to variant calling performance which is superior to that using the uncompressed file. We envisage our findings and framework serving as a benchmark in future development and analyses of lossy genomic data compressors. The \emph{Supplementary Data} can be found at \url{http://web.stanford.edu/~iochoa/supplementEffectLossy.zip}.

In spite of decades of linkage and association studies and its potential impact on human health, reliable prediction of an individual’s risk for heritable disease remains difficult. Large numbers of mapped loci do not explain substantial fractions of the heritable variation, leaving an open question of whether accurate complex trait predictions can be achieved in practice. Here, we use a full genome sequenced population of 7396 yeast strains of varying relatedness, and predict growth traits from family information, effects of segregating genetic variants, and growth measurements in other environments with an average coefficient of determination R2 of 0.91. This accuracy exceeds narrow-sense heritability, approaches limits imposed by measurement repeatability, and is higher than achieved with a single replicate assay in the lab. We find that both relatedness and variant-based predictions are greatly aided by availability of closer relatives, while information from a large number of more distant relatives does not improve predictive performance when close relatives can be used. Our results prove that very accurate prediction of heritable traits is possible, and recommend prioritizing collection of deeper family-based data over large reference cohorts.

Halite endoliths in the Atacama Desert represent one of the most extreme microbial ecosystems on Earth. Here we sequenced and characterized a shotgun metagenome from halite nodules collected in Salar Grande, Chile. The community is dominated by archaea and functional analysis attributed most of the autotrophic CO2 fixation to a unique cyanobacterium. The assembled 1.1 Mbp genome of a novel nanohaloarchaeon, Candidatus Nanopetramus SG9, revealed a photoheterotrophic life style and a low median isoelectric point (pI) for all predicted proteins, suggesting a “salt-in” strategy for osmotic balance. Predicted proteins of the algae identified in the community also had pI distributions similar to “salt-in” strategists. The Nanopetramus genome contained a unique CRISPR/Cas system with a spacer that matched a partial viral genome from the metagenome. A combination of reference-independent methods identified over 30 complete or near complete viral or proviral genomes with diverse genome structure, genome size, gene content, and hosts. Putative hosts included Halobacteriaceae, Nanohaloarchaea, and Cyanobacteria. Despite the dependence of the halite community on deliquescence for liquid water availability, this study exposed an ecosystem spanning three phylogenetic domains, containing a large diversity of viruses, and a predominant salt-in strategy to balance the high osmotic pressure of the environment.