%0 Journal Article %J Am J Hum Genet %D 2017 %T Whole-Genome Sequencing Coupled to Imputation Discovers Genetic Signals for Anthropometric Traits. %A Tachmazidou, Ioanna %A Süveges, Dániel %A Min, Josine L %A Ritchie, Graham R S %A Steinberg, Julia %A Walter, Klaudia %A Iotchkova, Valentina %A Schwartzentruber, Jeremy %A Huang, Jie %A Memari, Yasin %A McCarthy, Shane %A Crawford, Andrew A %A Bombieri, Cristina %A Cocca, Massimiliano %A Farmaki, Aliki-Eleni %A Gaunt, Tom R %A Jousilahti, Pekka %A Kooijman, Marjolein N %A Lehne, Benjamin %A Malerba, Giovanni %A Männistö, Satu %A Matchan, Angela %A Medina-Gomez, Carolina %A Metrustry, Sarah J %A Nag, Abhishek %A Ntalla, Ioanna %A Paternoster, Lavinia %A Rayner, Nigel W %A Sala, Cinzia %A Scott, William R %A Shihab, Hashem A %A Southam, Lorraine %A St Pourcain, Beate %A Traglia, Michela %A Trajanoska, Katerina %A Zaza, Gialuigi %A Zhang, Weihua %A Artigas, María S %A Bansal, Narinder %A Benn, Marianne %A Chen, Zhongsheng %A Danecek, Petr %A Lin, Wei-Yu %A Locke, Adam %A Luan, Jian'an %A Manning, Alisa K %A Mulas, Antonella %A Sidore, Carlo %A Tybjaerg-Hansen, Anne %A Varbo, Anette %A Zoledziewska, Magdalena %A Finan, Chris %A Hatzikotoulas, Konstantinos %A Hendricks, Audrey E %A Kemp, John P %A Moayyeri, Alireza %A Panoutsopoulou, Kalliope %A Szpak, Michal %A Wilson, Scott G %A Boehnke, Michael %A Cucca, Francesco %A Di Angelantonio, Emanuele %A Langenberg, Claudia %A Lindgren, Cecilia %A McCarthy, Mark I %A Morris, Andrew P %A Nordestgaard, Børge G %A Scott, Robert A %A Tobin, Martin D %A Wareham, Nicholas J %A Burton, Paul %A Chambers, John C %A Smith, George Davey %A Dedoussis, George %A Felix, Janine F %A Franco, Oscar H %A Gambaro, Giovanni %A Gasparini, Paolo %A Hammond, Christopher J %A Hofman, Albert %A Jaddoe, Vincent W V %A Kleber, Marcus %A Kooner, Jaspal S %A Perola, Markus %A Relton, Caroline %A Ring, Susan M %A Rivadeneira, Fernando %A Salomaa, Veikko %A Spector, Timothy D %A Stegle, Oliver %A Toniolo, Daniela %A Uitterlinden, André G %A Barroso, Inês %A Greenwood, Celia M T %A Perry, John R B %A Walker, Brian R %A Butterworth, Adam S %A Xue, Yali %A Durbin, Richard %A Small, Kerrin S %A Soranzo, Nicole %A Timpson, Nicholas J %A Zeggini, Eleftheria %K Anthropometry %K Body Height %K Cohort Studies %K Databases, Genetic %K DNA Methylation %K Female %K Genetic Variation %K Genome, Human %K Genome-Wide Association Study %K Humans %K Lipodystrophy %K Male %K Meta-Analysis as Topic %K Obesity %K Physical Chromosome Mapping %K Quantitative Trait Loci %K Sequence Analysis, DNA %K Sex Characteristics %K Syndrome %K United Kingdom %X

Deep sequence-based imputation can enhance the discovery power of genome-wide association studies by assessing previously unexplored variation across the common- and low-frequency spectra. We applied a hybrid whole-genome sequencing (WGS) and deep imputation approach to examine the broader allelic architecture of 12 anthropometric traits associated with height, body mass, and fat distribution in up to 267,616 individuals. We report 106 genome-wide significant signals that have not been previously identified, including 9 low-frequency variants pointing to functional candidates. Of the 106 signals, 6 are in genomic regions that have not been implicated with related traits before, 28 are independent signals at previously reported regions, and 72 represent previously reported signals for a different anthropometric trait. 71% of signals reside within genes and fine mapping resolves 23 signals to one or two likely causal variants. We confirm genetic overlap between human monogenic and polygenic anthropometric traits and find signal enrichment in cis expression QTLs in relevant tissues. Our results highlight the potential of WGS strategies to enhance biologically relevant discoveries across the frequency spectrum.

%B Am J Hum Genet %V 100 %P 865-884 %8 2017 Jun 01 %G eng %N 6 %1 http://www.ncbi.nlm.nih.gov/pubmed/28552196?dopt=Abstract %R 10.1016/j.ajhg.2017.04.014 %0 Journal Article %J Nat Commun %D 2015 %T Improved imputation of low-frequency and rare variants using the UK10K haplotype reference panel. %A Huang, Jie %A Howie, Bryan %A McCarthy, Shane %A Memari, Yasin %A Walter, Klaudia %A Min, Josine L %A Danecek, Petr %A Malerba, Giovanni %A Trabetti, Elisabetta %A Zheng, Hou-Feng %A Gambaro, Giovanni %A Richards, J Brent %A Durbin, Richard %A Timpson, Nicholas J %A Marchini, Jonathan %A Soranzo, Nicole %X

Imputing genotypes from reference panels created by whole-genome sequencing (WGS) provides a cost-effective strategy for augmenting the single-nucleotide polymorphism (SNP) content of genome-wide arrays. The UK10K Cohorts project has generated a data set of 3,781 whole genomes sequenced at low depth (average 7x), aiming to exhaustively characterize genetic variation down to 0.1% minor allele frequency in the British population. Here we demonstrate the value of this resource for improving imputation accuracy at rare and low-frequency variants in both a UK and an Italian population. We show that large increases in imputation accuracy can be achieved by re-phasing WGS reference panels after initial genotype calling. We also present a method for combining WGS panels to improve variant coverage and downstream imputation accuracy, which we illustrate by integrating 7,562 WGS haplotypes from the UK10K project with 2,184 haplotypes from the 1000 Genomes Project. Finally, we introduce a novel approximation that maintains speed without sacrificing imputation accuracy for rare variants.

%B Nat Commun %V 6 %P 8111 %8 2015 %G eng %1 http://www.ncbi.nlm.nih.gov/pubmed/26368830?dopt=Abstract %R 10.1038/ncomms9111