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Institution

Government of Victoria

GovernmentMelbourne, Victoria, Australia
About: Government of Victoria is a government organization based out in Melbourne, Victoria, Australia. It is known for research contribution in the topics: Population & Health care. The organization has 118 authors who have published 94 publications receiving 8423 citations.


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Journal ArticleDOI
Andrew R. Wood1, Tõnu Esko2, Jian Yang3, Sailaja Vedantam4  +441 moreInstitutions (132)
TL;DR: This article identified 697 variants at genome-wide significance that together explained one-fifth of the heritability for adult height, and all common variants together captured 60% of heritability.
Abstract: Using genome-wide data from 253,288 individuals, we identified 697 variants at genome-wide significance that together explained one-fifth of the heritability for adult height. By testing different numbers of variants in independent studies, we show that the most strongly associated ∼2,000, ∼3,700 and ∼9,500 SNPs explained ∼21%, ∼24% and ∼29% of phenotypic variance. Furthermore, all common variants together captured 60% of heritability. The 697 variants clustered in 423 loci were enriched for genes, pathways and tissue types known to be involved in growth and together implicated genes and pathways not highlighted in earlier efforts, such as signaling by fibroblast growth factors, WNT/β-catenin and chondroitin sulfate-related genes. We identified several genes and pathways not previously connected with human skeletal growth, including mTOR, osteoglycin and binding of hyaluronic acid. Our results indicate a genetic architecture for human height that is characterized by a very large but finite number (thousands) of causal variants.

1,872 citations

Journal ArticleDOI
Hana Lango Allen1, Karol Estrada2, Guillaume Lettre3, Sonja I. Berndt4  +341 moreInstitutions (90)
14 Oct 2010-Nature
TL;DR: It is shown that hundreds of genetic variants, in at least 180 loci, influence adult height, a highly heritable and classic polygenic trait, and indicates that GWA studies can identify large numbers of loci that implicate biologically relevant genes and pathways.
Abstract: Most common human traits and diseases have a polygenic pattern of inheritance: DNA sequence variants at many genetic loci influence the phenotype. Genome-wide association (GWA) studies have identified more than 600 variants associated with human traits, but these typically explain small fractions of phenotypic variation, raising questions about the use of further studies. Here, using 183,727 individuals, we show that hundreds of genetic variants, in at least 180 loci, influence adult height, a highly heritable and classic polygenic trait. The large number of loci reveals patterns with important implications for genetic studies of common human diseases and traits. First, the 180 loci are not random, but instead are enriched for genes that are connected in biological pathways (P = 0.016) and that underlie skeletal growth defects (P < 0.001). Second, the likely causal gene is often located near the most strongly associated variant: in 13 of 21 loci containing a known skeletal growth gene, that gene was closest to the associated variant. Third, at least 19 loci have multiple independently associated variants, suggesting that allelic heterogeneity is a frequent feature of polygenic traits, that comprehensive explorations of already-discovered loci should discover additional variants and that an appreciable fraction of associated loci may have been identified. Fourth, associated variants are enriched for likely functional effects on genes, being over-represented among variants that alter amino-acid structure of proteins and expression levels of nearby genes. Our data explain approximately 10% of the phenotypic variation in height, and we estimate that unidentified common variants of similar effect sizes would increase this figure to approximately 16% of phenotypic variation (approximately 20% of heritable variation). Although additional approaches are needed to dissect the genetic architecture of polygenic human traits fully, our findings indicate that GWA studies can identify large numbers of loci that implicate biologically relevant genes and pathways.

1,768 citations

Journal ArticleDOI
TL;DR: The results provide further evidence that a substantial proportion of heritability is captured by common SNPs, that height, BMI and QTi are highly polygenic traits, and that the additive variation explained by a part of the genome is approximately proportional to the total length of DNA contained within genes therein.
Abstract: We estimate and partition genetic variation for height, body mass index (BMI), von Willebrand factor and QT interval (QTi) using 586,898 SNPs genotyped on 11,586 unrelated individuals. We estimate that ∼45%, ∼17%, ∼25% and ∼21% of the variance in height, BMI, von Willebrand factor and QTi, respectively, can be explained by all autosomal SNPs and a further ∼0.5-1% can be explained by X chromosome SNPs. We show that the variance explained by each chromosome is proportional to its length, and that SNPs in or near genes explain more variation than SNPs between genes. We propose a new approach to estimate variation due to cryptic relatedness and population stratification. Our results provide further evidence that a substantial proportion of heritability is captured by common SNPs, that height, BMI and QTi are highly polygenic traits, and that the additive variation explained by a part of the genome is approximately proportional to the total length of DNA contained within genes therein.

912 citations

Journal ArticleDOI
Iris M. Heid1, Anne U. Jackson2, Joshua C. Randall3, Tthomas W. Winkler1  +352 moreInstitutions (90)
TL;DR: A meta-analysis of genome-wide association studies for WHR adjusted for body mass index provides evidence for multiple loci that modulate body fat distribution independent of overall adiposity and reveal strong gene-by-sex interactions.
Abstract: Waist-hip ratio (WHR) is a measure of body fat distribution and a predictor of metabolic consequences independent of overall adiposity. WHR is heritable, but few genetic variants influencing this trait have been identified. We conducted a meta-analysis of 32 genome-wide association studies for WHR adjusted for body mass index (comprising up to 77,167 participants), following up 16 loci in an additional 29 studies (comprising up to 113,636 subjects). We identified 13 new loci in or near RSPO3, VEGFA, TBX15-WARS2, NFE2L3, GRB14, DNM3-PIGC, ITPR2-SSPN, LY86, HOXC13, ADAMTS9, ZNRF3-KREMEN1, NISCH-STAB1 and CPEB4 (P = 1.9 × 10⁻⁹ to P = 1.8 × 10⁻⁴⁰) and the known signal at LYPLAL1. Seven of these loci exhibited marked sexual dimorphism, all with a stronger effect on WHR in women than men (P for sex difference = 1.9 × 10⁻³ to P = 1.2 × 10⁻¹³). These findings provide evidence for multiple loci that modulate body fat distribution independent of overall adiposity and reveal strong gene-by-sex interactions.

869 citations

Journal ArticleDOI
Richard A. Gibbs1, Jeremy F. Taylor2, Curtis P. Van Tassell3, William Barendse4, William Barendse5, Kellye Eversole, Clare A. Gill6, Ronnie D. Green3, Debora L. Hamernik3, Steven M. Kappes3, Sigbjørn Lien7, Lakshmi K. Matukumalli3, Lakshmi K. Matukumalli8, John C. McEwan9, Lynne V. Nazareth1, Robert D. Schnabel2, George M. Weinstock1, David A. Wheeler1, Paolo Ajmone-Marsan10, Paul Boettcher11, Alexandre Rodrigues Caetano12, José Fernando Garcia13, José Fernando Garcia11, Olivier Hanotte14, Paola Mariani15, Loren C. Skow6, Tad S. Sonstegard3, John L. Williams15, John L. Williams16, Boubacar Diallo, Lemecha Hailemariam17, Mário Luiz Martinez12, C. A. Morris9, Luiz Otávio Campos da Silva12, Richard J. Spelman18, Woudyalew Mulatu14, Keyan Zhao19, Colette A. Abbey6, Morris Agaba14, Flábio R. Araújo12, Rowan J. Bunch5, Rowan J. Bunch4, James O. Burton16, C. Gorni15, Hanotte Olivier15, Blair E. Harrison4, Blair E. Harrison5, Bill Luff, Marco Antonio Machado12, Joel Mwakaya14, Graham Plastow20, Warren Sim4, Warren Sim5, Timothy P. L. Smith3, Merle B Thomas5, Merle B Thomas4, Alessio Valentini21, Paul D. Williams4, James E. Womack6, John Woolliams16, Yue Liu1, Xiang Qin1, Kim C. Worley1, Chuan Gao6, Huaiyang Jiang1, Stephen S. Moore20, Yanru Ren1, Xingzhi Song1, Carlos Bustamante19, Ryan D. Hernandez19, Donna M. Muzny1, Shobha Patil1, Anthony San Lucas1, Qing Fu1, Matthew Peter Kent7, Richard Vega1, Aruna Matukumalli3, Sean McWilliam5, Sean McWilliam4, Gert Sclep15, Katarzyna Bryc19, Jung-Woo Choi6, Hong Gao19, John J. Grefenstette8, Brenda M. Murdoch20, Alessandra Stella15, Rafael Villa-Angulo8, Mark G. Wright19, Jan Aerts16, Jan Aerts22, Oliver C. Jann16, Riccardo Negrini10, Michael E. Goddard23, Michael E. Goddard24, Ben J. Hayes23, Daniel G. Bradley25, Marcos V.B. da Silva12, Marcos V.B. da Silva3, Lilian P.L. Lau25, George E. Liu3, David J. Lynn25, David J. Lynn26, Francesca Panzitta15, Ken G. Dodds9 
24 Apr 2009-Science
TL;DR: Data show that cattle have undergone a rapid recent decrease in effective population size from a very large ancestral population, possibly due to bottlenecks associated with domestication, selection, and breed formation.
Abstract: The imprints of domestication and breed development on the genomes of livestock likely differ from those of companion animals. A deep draft sequence assembly of shotgun reads from a single Hereford female and comparative sequences sampled from six additional breeds were used to develop probes to interrogate 37,470 single-nucleotide polymorphisms (SNPs) in 497 cattle from 19 geographically and biologically diverse breeds. These data show that cattle have undergone a rapid recent decrease in effective population size from a very large ancestral population, possibly due to bottlenecks associated with domestication, selection, and breed formation. Domestication and artificial selection appear to have left detectable signatures of selection within the cattle genome, yet the current levels of diversity within breeds are at least as great as exists within humans.

769 citations


Authors

Showing all 118 results

NameH-indexPapersCitations
Jacqueline Batley119121268752
Michael E. Goddard10642467681
Lu Qi9456654866
P. Eline Slagboom9338937286
David Edwards8970335570
Ben J. Hayes8034627872
Tim D. Spector801570182188
John W. Forster481716703
German Spangenberg473407906
Markus Perola4657378670
Noel O. I. Cogan361443911
Hans D. Daetwyler361376511
Alexandra Martiniuk341384126
Joe Panozzo331073528
Terence I. Walker33753466
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Performance
Metrics
No. of papers from the Institution in previous years
YearPapers
202114
20206
20197
20185
20178
20162