Sequencing and de novo assembly of 150 genomes from Denmark as a population reference

Lasse Maretty(University of Copenhagen), Jacob Malte Jensen(Aarhus University), Bent Petersen(Technical University of Denmark), Jonas A. Sibbesen(University of Copenhagen), Siyang Liu(University of Copenhagen), Palle Villesen(Aarhus University), Laurits Skov(Aarhus University), Kirstine Belling(Technical University of Denmark), Christian Theil Have(University of Copenhagen), José M. G. Izarzugaza(Technical University of Denmark), Marie Grosjean(Technical University of Denmark), Jette Bork‐Jensen(University of Copenhagen), Jakob Grove(Aarhus University), Thomas D. Als(Aarhus University), Shujia Huang(BGI Group (China)), Yuqi Chang(BGI Group (China)), Ruiqi Xu(BGI Europe (Denmark)), Weijian Ye(BGI Europe (Denmark)), Junhua Rao(BGI Europe (Denmark)), Xiaosen Guo(BGI Group (China)), Jihua Sun(Novo Nordisk Foundation), Hongzhi Cao(BGI Group (China)), Chen Ye(BGI Group (China)), Johan van Beusekom(Technical University of Denmark), Thomas Espeseth(University of Oslo), Esben N. Flindt(University of Copenhagen), Rune M. Friborg(Aarhus University), Anders E. Halager(Aarhus University), Stéphanie Le Hellard(Haukeland University Hospital), Christina M. Hultman(Karolinska Institutet), Francesco Lescai(Aarhus University), Shengting Li(Aarhus University), Ole Lund(Technical University of Denmark), Peter Løngren(Technical University of Denmark), Thomas Mailund(Aarhus University), María Luisa Matey-Hernandez(Technical University of Denmark), Ole Mors(Aarhus University), Christian N. S. Pedersen(Aarhus University), Thomas Sicheritz‐Pontén(Technical University of Denmark), Patrick F. Sullivan(University of North Carolina at Chapel Hill), Ali Syed(Technical University of Denmark), David Westergaard(Technical University of Denmark), Rachita Yadav(Technical University of Denmark), Ning Li(BGI Europe (Denmark)), Xun Xu(BGI Group (China)), Torben Hansen(University of Copenhagen), Anders Krogh(University of Copenhagen), Lars Bolund(BGI Group (China)), Thorkild I. A. Sørensen(University of Copenhagen), Oluf Pedersen(University of Copenhagen), Ramneek Gupta(Technical University of Denmark), Simon Rasmussen(Technical University of Denmark), Søren Besenbacher(Aarhus University), Anders D. Børglum(Aarhus University), Jun Wang(BGI Group (China)), Hans Eiberg(University of Copenhagen), Karsten Kristiansen(BGI Group (China)), Søren Brunak(University of Copenhagen), Mikkel Heide Schierup(Aarhus University)
Nature
July 25, 2017
Cited by 174Open Access
Full Text

Abstract

Hundreds of thousands of human genomes are now being sequenced to characterize genetic variation and use this information to augment association mapping studies of complex disorders and other phenotypic traits. Genetic variation is identified mainly by mapping short reads to the reference genome or by performing local assembly. However, these approaches are biased against discovery of structural variants and variation in the more complex parts of the genome. Hence, large-scale de novo assembly is needed. Here we show that it is possible to construct excellent de novo assemblies from high-coverage sequencing with mate-pair libraries extending up to 20 kilobases. We report de novo assemblies of 150 individuals (50 trios) from the GenomeDenmark project. The quality of these assemblies is similar to those obtained using the more expensive long-read technology. We use the assemblies to identify a rich set of structural variants including many novel insertions and demonstrate how this variant catalogue enables further deciphering of known association mapping signals. We leverage the assemblies to provide 100 completely resolved major histocompatibility complex haplotypes and to resolve major parts of the Y chromosome. Our study provides a regional reference genome that we expect will improve the power of future association mapping studies and hence pave the way for precision medicine initiatives, which now are being launched in many countries including Denmark.


Related Papers

No related papers found

Powered by citation graph analysis