%0 Journal Article %J Nat Commun %D 2017 %T Enrichment of low-frequency functional variants revealed by whole-genome sequencing of multiple isolated European populations. %A Xue, Yali %A Mezzavilla, Massimo %A Haber, Marc %A McCarthy, Shane %A Chen, Yuan %A Narasimhan, Vagheesh %A Gilly, Arthur %A Ayub, Qasim %A Colonna, Vincenza %A Southam, Lorraine %A Finan, Christopher %A Massaia, Andrea %A Chheda, Himanshu %A Palta, Priit %A Ritchie, Graham %A Asimit, Jennifer %A Dedoussis, George %A Gasparini, Paolo %A Palotie, Aarno %A Ripatti, Samuli %A Soranzo, Nicole %A Toniolo, Daniela %A Wilson, James F %A Durbin, Richard %A Tyler-Smith, Chris %A Zeggini, Eleftheria %K European Continental Ancestry Group %K Gene Frequency %K Genetic Variation %K Genetics, Population %K Genome, Human %K Humans %K Polymorphism, Single Nucleotide %K Whole Genome Sequencing %X

The genetic features of isolated populations can boost power in complex-trait association studies, and an in-depth understanding of how their genetic variation has been shaped by their demographic history can help leverage these advantageous characteristics. Here, we perform a comprehensive investigation using 3,059 newly generated low-depth whole-genome sequences from eight European isolates and two matched general populations, together with published data from the 1000 Genomes Project and UK10K. Sequencing data give deeper and richer insights into population demography and genetic characteristics than genotype-chip data, distinguishing related populations more effectively and allowing their functional variants to be studied more fully. We demonstrate relaxation of purifying selection in the isolates, leading to enrichment of rare and low-frequency functional variants, using novel statistics, DVxy and SVxy. We also develop an isolation-index (Isx) that predicts the overall level of such key genetic characteristics and can thus help guide population choice in future complex-trait association studies.

%B Nat Commun %V 8 %P 15927 %8 2017 06 23 %G eng %1 http://www.ncbi.nlm.nih.gov/pubmed/28643794?dopt=Abstract %R 10.1038/ncomms15927 %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 Genet %D 2016 %T A reference panel of 64,976 haplotypes for genotype imputation. %A McCarthy, Shane %A Das, Sayantan %A Kretzschmar, Warren %A Delaneau, Olivier %A Wood, Andrew R %A Teumer, Alexander %A Kang, Hyun Min %A Fuchsberger, Christian %A Danecek, Petr %A Sharp, Kevin %A Luo, Yang %A Sidore, Carlo %A Kwong, Alan %A Timpson, Nicholas %A Koskinen, Seppo %A Vrieze, Scott %A Scott, Laura J %A Zhang, He %A Mahajan, Anubha %A Veldink, Jan %A Peters, Ulrike %A Pato, Carlos %A van Duijn, Cornelia M %A Gillies, Christopher E %A Gandin, Ilaria %A Mezzavilla, Massimo %A Gilly, Arthur %A Cocca, Massimiliano %A Traglia, Michela %A Angius, Andrea %A Barrett, Jeffrey C %A Boomsma, Dorrett %A Branham, Kari %A Breen, Gerome %A Brummett, Chad M %A Busonero, Fabio %A Campbell, Harry %A Chan, Andrew %A Chen, Sai %A Chew, Emily %A Collins, Francis S %A Corbin, Laura J %A Smith, George Davey %A Dedoussis, George %A Dörr, Marcus %A Farmaki, Aliki-Eleni %A Ferrucci, Luigi %A Forer, Lukas %A Fraser, Ross M %A Gabriel, Stacey %A Levy, Shawn %A Groop, Leif %A Harrison, Tabitha %A Hattersley, Andrew %A Holmen, Oddgeir L %A Hveem, Kristian %A Kretzler, Matthias %A Lee, James C %A McGue, Matt %A Meitinger, Thomas %A Melzer, David %A Min, Josine L %A Mohlke, Karen L %A Vincent, John B %A Nauck, Matthias %A Nickerson, Deborah %A Palotie, Aarno %A Pato, Michele %A Pirastu, Nicola %A McInnis, Melvin %A Richards, J Brent %A Sala, Cinzia %A Salomaa, Veikko %A Schlessinger, David %A Schoenherr, Sebastian %A Slagboom, P Eline %A Small, Kerrin %A Spector, Timothy %A Stambolian, Dwight %A Tuke, Marcus %A Tuomilehto, Jaakko %A Van den Berg, Leonard H %A van Rheenen, Wouter %A Völker, Uwe %A Wijmenga, Cisca %A Toniolo, Daniela %A Zeggini, Eleftheria %A Gasparini, Paolo %A Sampson, Matthew G %A Wilson, James F %A Frayling, Timothy %A de Bakker, Paul I W %A Swertz, Morris A %A McCarroll, Steven %A Kooperberg, Charles %A Dekker, Annelot %A Altshuler, David %A Willer, Cristen %A Iacono, William %A Ripatti, Samuli %A Soranzo, Nicole %A Walter, Klaudia %A Swaroop, Anand %A Cucca, Francesco %A Anderson, Carl A %A Myers, Richard M %A Boehnke, Michael %A McCarthy, Mark I %A Durbin, Richard %X

We describe a reference panel of 64,976 human haplotypes at 39,235,157 SNPs constructed using whole-genome sequence data from 20 studies of predominantly European ancestry. Using this resource leads to accurate genotype imputation at minor allele frequencies as low as 0.1% and a large increase in the number of SNPs tested in association studies, and it can help to discover and refine causal loci. We describe remote server resources that allow researchers to carry out imputation and phasing consistently and efficiently.

%B Nat Genet %8 2016 Aug 22 %G ENG %1 http://www.ncbi.nlm.nih.gov/pubmed/27548312?dopt=Abstract %R 10.1038/ng.3643 %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