Detection of simple and complex de novo mutations with multiple reference sequences.

The characterization of de novo mutations in regions of high sequence and structural diversity from whole-genome sequencing data remains highly challenging. Complex structural variants tend to arise in regions of high repetitiveness and low complexity, challenging both de novo assembly, in which short reads do not capture the long-range context required for resolution, and mapping approaches, in which improper alignment of reads to a reference genome that is highly diverged from that of the sample can lead to false or partial calls. Long-read technologies can potentially solve such problems but are currently unfeasible to use at scale. Here we present Corticall, a graph-based method that combines the advantages of multiple technologies and prior data sources to detect arbitrary classes of genetic variant. We construct multisample, colored de Bruijn graphs from short-read data for all samples, align long-read-derived haplotypes and multiple reference data sources to restore graph connectivity information, and call variants using graph path-finding algorithms and a model for simultaneous alignment and recombination. We validate and evaluate the approach using extensive simulations and use it to characterize the rate and spectrum of de novo mutation events in 119 progeny from four Plasmodium falciparum experimental crosses, using long-read data on the parents to inform reconstructions of the progeny and to detect several known and novel nonallelic homologous recombination events

A framework for the detection of de novo mutations in family-based sequencing data

Francioli LC, Cretu-Stancu M, Garimella KV, et al. A framework for the detection of de novo mutations in family-based sequencing data. European Journal of Human Genetics. 2016;25(2):227-233

The functional spectrum of low-frequency coding variation

Background Rare coding variants constitute an important class of human genetic variation, but are underrepresented in current databases that are based on small population samples. Recent studies show that variants altering amino acid sequence and protein function are enriched at low variant allele frequency, 2 to 5%, but because of insufficient sample size it is not clear if the same trend holds for rare variants below 1% allele frequency. Results The 1000 Genomes Exon Pilot Project has collected deep-coverage exon-capture data in roughly 1,000 human genes, for nearly 700 samples. Although medical whole-exome projects are currently afoot, this is still the deepest reported sampling of a large number of human genes with next-generation technologies. According to the goals of the 1000 Genomes Project, we created effective informatics pipelines to process and analyze the data, and discovered 12,758 exonic SNPs, 70% of them novel, and 74% below 1% allele frequency in the seven population samples we examined. Our analysis confirms that coding variants below 1% allele frequency show increased population-specificity and are enriched for functional variants. Conclusions This study represents a large step toward detecting and interpreting low frequency coding variation, clearly lays out technical steps for effective analysis of DNA capture data, and articulates functional and population properties of this important class of genetic variatio

Mapping Copy Number Variation by Population Scale Genome Sequencing

Genomic structural variants (SVs) are abundant in humans, differing from other forms of variation in extent, origin and functional impact. Despite progress in SV characterization, the nucleotide resolution architecture of most SVs remains unknown. We constructed a map of unbalanced SVs (that is, copy number variants) based on whole genome DNA sequencing data from 185 human genomes, integrating evidence from complementary SV discovery approaches with extensive experimental validations. Our map encompassed 22,025 deletions and 6,000 additional SVs, including insertions and tandem duplications. Most SVs (53%) were mapped to nucleotide resolution, which facilitated analysing their origin and functional impact. We examined numerous whole and partial gene deletions with a genotyping approach and observed a depletion of gene disruptions amongst high frequency deletions. Furthermore, we observed differences in the size spectra of SVs originating from distinct formation mechanisms, and constructed a map of SV hotspots formed by common mechanisms. Our analytical framework and SV map serves as a resource for sequencing-based association studies.Organismic and Evolutionary Biolog

A framework for the detection of de novo mutations in family-based sequencing data

Germline mutation detection from human DNA sequence data is challenging due to the rarity of such events relative to the intrinsic error rates of sequencing technologies and the uneven coverage across the genome. We developed PhaseByTransmission (PBT) to identify de novo single nucleotide variants and short insertions and deletions (indels) from sequence data collected in parent-offspring trios. We compute the joint probability of the data given the genotype likelihoods in the individual family members, the known familial relationships and a prior probability for the mutation rate. Candidate de novo mutations (DNMs) are reported along with their posterior probability, providing a systematic way to prioritize them for validation. Our tool is integrated in the Genome Analysis Toolkit and can be used together with the ReadBackedPhasing module to infer the parental origin of DNMs based on phase-informative reads. Using simulated data, we show that PBT outperforms existing tools, especially in low coverage data and on the X chromosome. We further show that PBT displays high validation rates on empirical parent-offspring sequencing data for whole-exome data from 104 trios and X-chromosome data from 249 parent-offspring families. Finally, we demonstrate an association between father's age at conception and the number of DNMs in female offspring's X chromosome, consistent with previous literature reports

A Beary Good Genome: Haplotype-Resolved, Chromosome-Level Assembly of the Brown Bear (Ursus arctos)

The brown bear (Ursus arctos) is the second largest and most widespread extant terrestrial carnivore on Earth and has recently emerged as a medical model for human metabolic diseases. Here, we report a fully phased chromosome-level assembly of a male North American brown bear built by combining Pacific Biosciences (PacBio) HiFi data and publicly available Hi-C data. The final genome size is 2.47 Gigabases (Gb) with a scaffold and contig N50 length of 70.08 and 43.94 Megabases (Mb), respectively. Benchmarking Universal Single-Copy Ortholog (BUSCO) analysis revealed that 94.5% of single copy orthologs from Mammalia were present in the genome (the highest of any ursid genome to date). Repetitive elements accounted for 44.48% of the genome and a total of 20,480 protein coding genes were identified. Based on whole genome alignment to the polar bear, the brown bear is highly syntenic with the polar bear, and our phylogenetic analysis of 7,246 single-copy orthologs supports the currently proposed species tree for Ursidae. This highly contiguous genome assembly will support future research on both the evolutionary history of the bear family and the physiological mechanisms behind hibernation, the latter of which has broad medical implications