Parent-of-origin detection and chromosome-scale haplotyping using long-read DNA methylation sequencing and Strand-seq.

Hundreds of loci in human genomes have alleles that are methylated differentially according to their parent of origin. These imprinted loci generally show little variation across tissues, individuals, and populations. We show that such loci can be used to distinguish the maternal and paternal homologs for all human autosomes without the need for the parental DNA. We integrate methylation-detecting nanopore sequencing with the long-range phase information in Strand-seq data to determine the parent of origin of chromosome-length haplotypes for both DNA sequence and DNA methylation in five trios with diverse genetic backgrounds. The parent of origin was correctly inferred for all autosomes with an average mismatch error rate of 0.31% for SNVs and 1.89% for insertions or deletions (indels). Because our method can determine whether an inherited disease allele originated from the mother or the father, we predict that it will improve the diagnosis and management of many genetic diseases.

Chromosome-Length Haplotypes with StrandPhaseR and Strand-seq.

Dense local haplotypes can now readily be extracted from long-read or droplet-based sequence data. However, these methods struggle to combine subchromosomal haplotype blocks into global chromosome-length haplotypes. Strand-seq is a single cell sequencing technique that uses read orientation to capture sparse global phase information by sequencing only one of two DNA strands for each parental homolog. In combination with dense local haplotypes from other technologies, Strand-seq data can be used to obtain complete chromosome-length phase information. In this chapter, we run the R package StrandPhaseR to phase SNVs using publicly available sequence data for sample HG005 of the Genome in a Bottle project.

A survey of current methods to detect and genotype inversions.

Polymorphic inversions are ubiquitous in humans and they have been linked to both adaptation and disease. Following their discovery in Drosophila more than a century ago, inversions have proved to be more elusive than other structural variants. A wide variety of methods for the detection and genotyping of inversions have recently been developed: multiple techniques based on selective amplification by PCR, short- and long-read sequencing approaches, principal component analysis of small variant haplotypes, template strand sequencing, optical mapping, and various genome assembly methods. Many methods apply complex wet lab protocols or increasingly refined bioinformatic analyses. This review is an attempt to provide a practical summary and comparison of the methods that are in current use, with a focus on metrics such as the maximum size of segmental duplications at inversion breakpoints that each method can tolerate, the size range of inversions that they recover, their throughput, and whether the locations of putative inversions must be known beforehand.

Construction of Strand-seq libraries in open nanoliter arrays.

Single-cell Strand-seq generates directional genomic information to study DNA repair, assemble genomes, and map structural variation onto chromosome-length haplotypes. We report a nanoliter-volume, one-pot (OP) Strand-seq library preparation protocol in which reagents are added cumulatively, DNA purification steps are avoided, and enzymes are inactivated with a thermolabile protease. OP-Strand-seq libraries capture 10%-25% of the genome from a single-cell with reduced costs and increased throughput.

InvertypeR: Bayesian inversion genotyping with Strand-seq data.

BACKGROUND: Single cell Strand-seq is a unique tool for the discovery and phasing of genomic inversions. Conventional methods to discover inversions with Strand-seq data are blind to known inversion locations, limiting their statistical power for the detection of inversions smaller than 10 Kb. Moreover, the methods rely on manual inspection to separate false and true positives. RESULTS: Here we describe "InvertypeR", a method based on a Bayesian binomial model that genotypes inversions using fixed genomic coordinates. We validated InvertypeR by re-genotyping inversions reported for three trios by the Human Genome Structural Variation Consortium. Although 6.3% of the family inversion genotypes in the original study showed Mendelian discordance, this was reduced to 0.5% using InvertypeR. By applying InvertypeR to published inversion coordinates and predicted inversion hotspots (n = 3701), as well as coordinates from conventional inversion discovery, we furthermore genotyped 66 inversions not previously reported for the three trios. CONCLUSIONS: InvertypeR discovers, genotypes, and phases inversions without relying on manual inspection. For greater accessibility, results are presented as phased chromosome ideograms with inversions linked to Strand-seq data in the genome browser. InvertypeR increases the power of Strand-seq for studies on the role of inversions in phenotypic variation, genome instability, and human disease.

Somatic mutations substantially increase the per-generation mutation rate in the conifer Picea sitchensis.

The rates and biological significance of somatic mutations have long been a subject of debate. Somatic mutations in plants are expected to accumulate with vegetative growth and time, yet rates of somatic mutations are unknown for conifers, which can reach exceptional sizes and ages. We investigated somatic mutation rates in the conifer Sitka spruce (Picea sitchensis (Bong.) Carr.) by analyzing a total of 276 Gb of nuclear DNA from the tops and bottoms of 20 old-growth trees averaging 76 m in height. We estimate a somatic base substitution rate of 2.7 × 10-8 per base pair within a generation. To date, this is one of the highest estimated per-generation rates of mutation among eukaryotes, indicating that somatic mutations contribute substantially to the total per-generation mutation rate in conifers. Nevertheless, as the sampled trees are centuries old, the per-year rate is low in comparison with nontree taxa. We argue that although somatic mutations raise genetic load in conifers, they generate important genetic variation and enable selection both among cell lineages within individual trees and among offspring.