Abstract
Key message
We present new association mapping methods which address the unique challenges of analyzing genome-wide data from multi-environment plant studies.
Abstract
Association studies on a genome-wide scale are being performed in plants. Unlike human studies, plant studies contain replicates whose data may be recorded across different environments. Plant studies also often employ elaborate experimental designs for controlling extraneous phenotypic variation. As a result, the genome-wide analysis of data from plant studies can be challenging. In this paper, we present QK-based association mapping for the analysis of data from plant association studies. In doing so, we have developed: (a) a general multivariate QK framework for association mapping in plant studies of arbitrary complexity; (b) a new weighted two-stage analysis approach for QK-based association mapping; (c) a heuristic procedure for determining when two-stage analysis is appropriate; and (d) a Monte Carlo sampling procedure for controlling the genome-wide type I error rate. We conduct a simulation study to evaluate the performance of our genome-wide mapping technique. We also analyze data from a multi-environment association study in wheat.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
References
Abecasis GR, Cardon LR, Cookson WOC (2000) A general test of association for quantitative traits in nuclear families. Am J Hum Genet 66(1):279–292
Butler DG, Cullis BR, Gilmour AR, Gogel BJ (2009) ASReml User Guide. URL: http://www.vsni.co.uk/software/asreml/
Churchill GA, Doerge RW (1994) Empirical threshold values for quantitative trait mapping. Genetics 138(3):963–971
Dudbridge F (2003) Pedigree disequilibrium tests for multilocus haplotypes. Genet Epidemiol 25(2):115–121
Dudoit S, Shaffer JP, Boldrick JC (2003) Multiple hypothesis testing in microarray experiments. Stat Sci 18(1):71–103
Flint-Garcia SA, Thornsberry JM, Buckler ES (2003) Structure of linkage disequilibrium in plants. Annu Rev Plant Biol 54:357–374
George AW (2013) Controlling type 1 error rates in genome-wide association studies in plants. Heredity 111:86–87
Hardy OJ, Vekemans X (2002) Spagedi: a versatile computer program to analyse spatial genetic structure at the individual or population levels. Mol Ecol Notes 2(4):618–620
Huang B, George A, Forrest K, Kilian A, Hayden M, MK M, Cavanagh C, (2012) A multiparent advanced generation inter-cross population for genetic analysis in wheat. J Plant Biot 10(7):826–839
Kang HM, Zaitlen NA, Wade CM, Kirby A, Heckerman D, Daly MJ, Eskin E (2008) Efficient control of population structure in model organism association mapping. Genetics 178:1709–1723
Lever T, Kelly A, De Faveri J, Martin D, Sheppard J, Quail K, Miskelly D (2005) Australian wheat for the sponge and dough bread making process. Aust J Agr Res 56(10):1049–1057
Lippert C, Listgarten J, Liu Y, Kadie CM, Davidson RI, Heckerman D (2011) Fast linear mixed models for genome-wide association studies. Nat Methods 8:833–835
Mohring J, Piepho HP (2009) Comparison of weighting in two-stage analysis of plant breeding trials. Crop Sci 49:1977–1988
Muller BU, Stich B, Piepho HP (2011) A general method for controlling the genome-wide type i error rate in linkage and association mapping experiments in plants. Heredity 106(5):825–831
Muller BU, Stich B, Piepho HP (2013) Response to controlling type 1 error rates in genome-wide association studies in plants. Heredity 111:88
North BV, Curtis D, Sham PC (2002) A note on the calculation of empirical p values from monte carlo procedures. Am J Hum Genet 71(2):439–441
Oakey H, Verbyla A, Pitchford W (2006) Joint modeling of additive and non-additive genetic line effects in single field trials. Theor Appl Genet 113:809–819
Piepho HP, Mohring J, Schulz-Streeck T, Ogutu J (2012) A stage-wise approach for the analysis of multi-environment trials. Biom J 54:844–860
Price AL, Patterson NJ, Plenge RM, Weinblatt ME, Shadick NA, Reich D (2006) Principal components analysis corrects for stratification in genome-wide association studies. Nat Genet 38:904–909
Pritchard JK, Stephens M, Donnelly P (2000a) Inference of population structure using multilocus genotype data. Genetics 155(2):945–959
Pritchard JK, Stephens M, Rosenberg NA, Donnelly P (2000b) Association mapping in structured populations. Am J Hum Genet 67(1):170–181
R Core Team (2013) A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria, URL: http://www.R-project.org/
Ritland K (1996) Estimators for pairwise relatedness and individual inbreeding coefficients. Genet Res 67(2):175–185
Rossini AJ, Tierney L, Li N (2007) Simple parallel statistical computing in r. J Comput Graph Stat 16(2):399–420
Sidak Z (1967) Rectangular confidence regions for the means of multivariate normal distributions. J Am Stat Assoc 62:626–633
Smith A, Cullis B, Gilmour A (2001a) The analysis of crop variety evaluation data in australia. Aust NZ J Stat 43(2):129–145
Smith A, Cullis B, Thompson R (2001b) Analyzing variety by environment data using multiplicative mixed models and adjustments for spatial field trend. Biometrics 57:1138–1147
Spielman RS, McGinnis RE, Ewens WJ (1993) Transmission test for linkage disequilibrium—the insulin gene region and insulin-dependent diabetes-mellitus (IDDM). Am J Hum Genet 52(3):506–516
Stich B, Mohring J, Piepho HP, Heckenberger M, Buckler ES, Melchinger AE (2008) Comparison of mixed-model approaches for association mapping. Genetics 178(3):1745–1754
Tierney L, Rossini AJ, Li N (2009) Snow: A parallel computing framework for the r system. Int J Parallel Prog 37(1):78–90
van Eeuwijk FA, Bink MCAM, Chenu K, Chapman SC (2010) Detection and use of qtl for complex traits in multiple environments. Curr Opin Plant Biol 13:193–205
Welham S, Gogel B, Smith A, Thompson R, Cullis B (2010) A comparison of analysis methods for late-stage variety evaluation trials. Aust NZ J Stat 52:125–149
Yu JM, Pressoir G, Briggs WH, Bi IV, Yamasaki M, Doebley JF, McMullen MD, Gaut BS, Nielsen DM, Holland JB, Kresovich S, Buckler ES (2006) A unified mixed-model method for association mapping that accounts for multiple levels of relatedness. Nat Genet 38(2):203–208
Zhao K, Aranzana MJ, Kim S, Lister C, Shindo C, Tang C, Toomajian C, Zheng H, Dean C, Marjoram P, Nordborg M (2007) An arabidopsis example of association mapping in structured samples. Plos Genetics 3(1)
Zhou X, Stephens M (2012) Genome-wide efficient mixed-model analysis for association studies. Nat Genet 44:821–824
Acknowledgments
We are grateful to Prof. Arunas Verbyla and Dr. Julian Taylor for many helpful discussions when developing this work, Dr. Alison Kelly for constructing base models of the phenotypic data, and the Department of Primary Industries for giving us access to the phenotypic data from the wheat study.
Conflict of interest
The authors declare no conflict of interest.
Author information
Authors and Affiliations
Corresponding author
Additional information
Communicated by M. J. Sillanpaa.
Electronic supplementary material
Below is the link to the electronic supplementary material.
Rights and permissions
About this article
Cite this article
George, A.W., Cavanagh, C. Genome-wide association mapping in plants. Theor Appl Genet 128, 1163–1174 (2015). https://doi.org/10.1007/s00122-015-2497-x
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s00122-015-2497-x