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Long-read sequencing of 3,622 Icelanders provides insight into the role of structural variants in human diseases and other traits


Date: 10th May 2021 | Source: Nature genetics

Authors: Doruk Beyter, Helga Ingimundardottir, Asmundur Oddsson, Hannes P. Eggertsson, Eythor Bjornsson, Hakon Jonsson, Bjarni A. Atlason, Snaedis Kristmundsdottir, Svenja Mehringer, Marteinn T. Hardarson, Sigurjon A. Gudjonsson, Droplaug N. Magnusdottir, Aslaug Jonasdottir, Adalbjorg Jonasdottir, Ragnar P. Kristjansson, Sverrir T. Sverrisson, Guillaume Holley, Gunnar Palsson, Olafur A. Stefansson, Gudmundur Eyjolfsson, Isleifur Olafsson, Olof Sigurdardottir, Bjarni Torfason, Gisli Masson, Agnar Helgason, Unnur Thorsteinsdottir, Hilma Holm, Daniel F. Gudbjartsson, Patrick Sulem, Olafur T. Magnusson, Bjarni V. Halldorsson, Kari Stefansson.

Long-read sequencing (LRS) promises to improve characterization of structural variants (SVs), a major source of genetic diversity. We generated LRS data on 3,622 Icelanders using Oxford Nanopore Technologies, and identified a median of 22,636 SVs per individual (a median of 13,353 insertions and 9,474 deletions), spanning a median of 10 Mb per haploid genome.

We discovered a set of 133,886 reliably genotyped SV alleles and imputed them into 166,281 individuals to explore their effects on diseases and other traits. We discovered an association with a rare (AF = 0.037%) deletion of the first exon of PCSK9. Carriers of this deletion have 0.93 mmol/L (1.31 SD) lower LDL cholesterol levels than the population average (p-value = 7.0·10−20). We also discovered an association with a multi-allelic SV inside a large repeat region, contained within single long reads, in an exon of ACAN. Within this repeat region we found 11 alleles that differ in the number of a 57 bp-motif repeat, and observed a linear relationship (0.016 SD per motif inserted, p = 6.2·10−18) between the number of repeats carried and height.

These results show that SVs can be accurately characterized at population scale using long read sequence data in a genome-wide non-targeted approach and demonstrate how SVs impact phenotypes.

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