Mapping copy number variation by population scale genome sequencing

By Ryan E. Mills, Klaudia Walter, Chip Stewart, Robert E. Handsaker, Ken Chen, Can Alkan, Alexej Abyzov, Seungtai Yoon, Kai Ye, R. K. Cheetham and 47 more. Nature, 2011

1,130 citations recorded by the source corpus.

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.

Subfields: Genetics, Plant Science

View Paper: https://www.semanticscholar.org/paper/30f19374cb4cb7f596f9fcfdba38a210d821cccb

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Genetics

In Plant Science

Citations in the corpus

Low concordance of multiple variant-calling pipelines: practical implications for exome and genome sequencing (2013, background): “Although the focus of most variant-calling software has been on detecting SNVs, it is the case that large-scale structural copy number variants and small indels are known to also be a biologically relevant and prevalent form of genetic variation [40,41].”
GRIDSS: sensitive and specific genomic rearrangement detection using positional de Bruijn graph assembly (2017, positive): “When compared to the Mills et al. (2011) call set, GRIDSS was able to identify the first 1000 true positives with a false discovery rate of 7% (3% using long-read validation data), compared to the next closest method, LUMPY, at 11% (7%).”
An integrated map of genetic variation from 1,092 human genomes (2012, background): “low-frequency variants in individuals from diverse, Recent efforts to map human 2–4 admixed, populations. origin, they exhibit increased levels of population differentiation 6– Characterizing such variants, for both point mutations and structural many variants of functional importance and is…”
Oxford Nanopore MinION Sequencing and Genome Assembly (2016, background): “Currently, variants of size P 50 bp in length are all considered as SVs [65–68].”
LUMPY: a probabilistic framework for structural variant discovery (2012, methodology): “The read-pair module maps the output of a paired-end sequence alignment algorithm such as NOVOALIGN (C Hercus, unpublished) [8] or BWA [9], the split-read module maps the output of a split-read sequence alignment algorithm (for example, YAHA [10], BWA-SW [11], or BWA-MEM [5]), and the generic module”
Natural variation in genome architecture among 205 Drosophila melanogaster Genetic Reference Panel lines (2014, methodology): “Methods combining several approaches to generate a consensus variant list give a lower false positive rate than individual methods (Mills et al., 2011; Zichner et al., 2013).”
Comprehensive evaluation of structural variation detection algorithms for whole genome sequencing (2019, methodology): “Mills et al. examined the accuracy of over-lapping calls between five methods and demonstrated that combining algorithms based on the same method increased precision, but the increase was lower than when combining algorithms based on different methods [14].”
A survey of tools for variant analysis of next-generation genome sequencing data (2013, neutral): “number of independent variant callers) [145].”