Marker-based estimation of heritability in immortal populations.
Willem Kruijer,Martin P. Boer,Marcos Malosetti,Pádraic J. Flood,Bas Engel,Rik Kooke,Joost J. B. Keurentjes,Joost J. B. Keurentjes,Fred A. van Eeuwijk +8 more
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Mixed models at the individual plant or plot level produced more realistic heritability estimates, and for simulated traits standard errors were up to 13 times smaller, and genomic prediction was improved by using these mixed models, with up to a 49% increase in accuracy.Abstract:
Heritability is a central parameter in quantitative genetics, from both an evolutionary and a breeding perspective. For plant traits heritability is traditionally estimated by comparing within- and between-genotype variability. This approach estimates broad-sense heritability and does not account for different genetic relatedness. With the availability of high-density markers there is growing interest in marker-based estimates of narrow-sense heritability, using mixed models in which genetic relatedness is estimated from genetic markers. Such estimates have received much attention in human genetics but are rarely reported for plant traits. A major obstacle is that current methodology and software assume a single phenotypic value per genotype, hence requiring genotypic means. An alternative that we propose here is to use mixed models at the individual plant or plot level. Using statistical arguments, simulations, and real data we investigate the feasibility of both approaches and how these affect genomic prediction with the best linear unbiased predictor and genome-wide association studies. Heritability estimates obtained from genotypic means had very large standard errors and were sometimes biologically unrealistic. Mixed models at the individual plant or plot level produced more realistic estimates, and for simulated traits standard errors were up to 13 times smaller. Genomic prediction was also improved by using these mixed models, with up to a 49% increase in accuracy. For genome-wide association studies on simulated traits, the use of individual plant data gave almost no increase in power. The new methodology is applicable to any complex trait where multiple replicates of individual genotypes can be scored. This includes important agronomic crops, as well as bacteria and fungi.read more
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References
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Journal Article
Human biochemical genetics
TL;DR: For the next few weeks the course is going to be exploring a field that’s actually older than classical population genetics, although the approach it’ll be taking to it involves the use of population genetic machinery.
Journal ArticleDOI
Genome-wide association study of 14,000 cases of seven common diseases and 3,000 shared controls
Paul Burton,David Clayton,Lon R. Cardon,Nicholas John Craddock,Panos Deloukas,Audrey Duncanson,Dominic P. Kwiatkowski,Mark I. McCarthy,Willem H. Ouwehand,Nilesh J. Samani,John A. Todd,Peter Donnelly,Jeffrey C. Barrett,Dan Davison,Doug Easton,David M. Evans,H. T. Leung,Jonathan Marchini,Andrew P. Morris,Chris C. A. Spencer,Martin D. Tobin,Antony P. Attwood,James P. Boorman,Barbara Cant,Ursula Everson,Judith M. Hussey,Jennifer Jolley,Alexandra S. Knight,Kerstin Koch,Elizabeth Meech,Sarah Nutland,Christopher Prowse,Helen Stevens,Niall C. Taylor,Graham R. Walters,Neil Walker,Nicholas A. Watkins,Thilo Winzer,Richard Jones,Wendy L. McArdle,Susan M. Ring,David P. Strachan,Marcus Pembrey,Gerome Breen,David St Clair,Sian Caesar,Katherine Gordon-Smith,Lisa Jones,Christine Fraser,Elaine K. Green,Detelina Grozeva,Marian L. Hamshere,Peter Holmans,Ian Jones,George Kirov,Valentina Moskvina,Ivan Nikolov,Michael Conlon O'Donovan,Michael John Owen,David A. Collier,Amanda Elkin,Anne Farmer,Richard Williamson,Peter McGuffin,Allan H. Young,I. Nicol Ferrier,Stephen G. Ball,Anthony J. Balmforth,Jennifer H. Barrett,D. Timothy Bishop,Mark M. Iles,Azhar Maqbool,Nadira Yuldasheva,Alistair S. Hall,Peter S. Braund,Richard J. Dixon,Massimo Mangino,Suzanne Stevens,John R. Thompson,Francesca Bredin,Mark Tremelling,Miles Parkes,Hazel E. Drummond,Charlie W. Lees,Elaine R. Nimmo,Jack Satsangi,Sheila A. Fisher,Alastair Forbes,Cathryn M. Lewis,Clive M. Onnie,Natalie J. Prescott,Jeremy D. Sanderson,Christopher G. Mathew,Jamie Barbour,M. Khalid Mohiuddin,Catherine E. Todhunter,John C. Mansfield,Tariq Ahmad,Fraser Cummings,Derek P. Jewell,John Webster,Morris J. Brown,G. Mark Lathrop,John M. C. Connell,Anna F. Dominiczak,Carolina A. Braga Marcano,Beverley Burke,Richard Dobson,Johannie Gungadoo,Kate L. Lee,Patricia B. Munroe,Stephen Newhouse,Abiodun Onipinla,Chris Wallace,Mingzhan Xue,Mark J. Caulfield,Martin Farrall,Anne Barton,Ian N. Bruce,Hannah Donovan,Steve Eyre,Paul D. Gilbert,Samantha L. Hider,Anne Hinks,Sally John,Catherine Potter,Alan J. Silman,Deborah P M Symmons,Wendy Thomson,Jane Worthington,David B. Dunger,Barry Widmer,Timothy M. Frayling,Rachel M. Freathy,Hana Lango,John R. B. Perry,Beverley M. Shields,Michael N. Weedon,Andrew T. Hattersley,Graham A. Hitman,Mark Walker,Kate S. Elliott,Christopher J. Groves,Cecilia M. Lindgren,Nigel W. Rayner,Nicholas J. Timpson,Eleftheria Zeggini,Melanie J. Newport,Giorgio Sirugo,Emily J. Lyons,Fredrik O. Vannberg,Adrian V. S. Hill,Linda A. Bradbury,C Farrar,J J Pointon,Paul Wordsworth,Matthew A. Brown,Jayne A. Franklyn,Joanne M. Heward,Matthew J. Simmonds,Stephen C. L. Gough,Sheila Seal,Michael R. Stratton,Nazneen Rahman,Maria Ban,An Goris,Stephen Sawcer,Alastair Compston,David J. Conway,Muminatou Jallow,Kirk A. Rockett,Suzannah Bumpstead,Amy Chaney,Kate Downes,Mohammed J. R. Ghori,Rhian Gwilliam,Sarah E. Hunt,Michael Inouye,Andrew Keniry,Emma King,Ralph McGinnis,Simon C. Potter,Rathi Ravindrarajah,Pamela Whittaker,Claire Widden,David Withers,Niall Cardin,Teresa Ferreira,Joanne Pereira-Gale,Ingileif B. Hallgrímsdóttir,Bryan Howie,Zhan Su,Yik Ying Teo,Damjan Vukcevic,David Bentley,A Compston +195 more
TL;DR: This study has demonstrated that careful use of a shared control group represents a safe and effective approach to GWA analyses of multiple disease phenotypes; generated a genome-wide genotype database for future studies of common diseases in the British population; and shown that, provided individuals with non-European ancestry are excluded, the extent of population stratification in theBritish population is generally modest.
Journal ArticleDOI
Finding the missing heritability of complex diseases
Teri A. Manolio,Francis S. Collins,Nancy J. Cox,David Goldstein,Lucia A. Hindorff,David J. Hunter,Mark I. McCarthy,Erin M. Ramos,Lon R. Cardon,Aravinda Chakravarti,Judy H. Cho,Alan E. Guttmacher,Augustine Kong,Leonid Kruglyak,Leonid Kruglyak,Elaine R. Mardis,Charles N. Rotimi,Montgomery Slatkin,David Valle,Alice S. Whittemore,Michael Boehnke,Andrew G. Clark,Evan E. Eichler,Greg Gibson,Jonathan L. Haines,Trudy F. C. Mackay,Steven A. McCarroll,Peter M. Visscher +27 more
TL;DR: This paper examined potential sources of missing heritability and proposed research strategies, including and extending beyond current genome-wide association approaches, to illuminate the genetics of complex diseases and enhance its potential to enable effective disease prevention or treatment.
Journal ArticleDOI
GCTA: a tool for genome-wide complex trait analysis.
TL;DR: The GCTA software is a versatile tool to estimate and partition complex trait variation with large GWAS data sets and focuses on the function of estimating the variance explained by all the SNPs on the X chromosome and testing the hypotheses of dosage compensation.
Journal ArticleDOI
TASSEL: software for association mapping of complex traits in diverse samples
Peter J. Bradbury,Zhiwu Zhang,Dallas E. Kroon,Terry M. Casstevens,Yogesh Ramdoss,Edward S. Buckler +5 more
TL;DR: TASSEL (Trait Analysis by aSSociation, Evolution and Linkage) implements general linear model and mixed linear model approaches for controlling population and family structure and allows for linkage disequilibrium statistics to be calculated and visualized graphically.