Abstract
Selective phenotyping is a way of capturing the benefits of large population sizes without the need to carry out large-scale phenotyping and hence is a cost-effective means of capturing information about gene–trait relationships within a population. The diversity within the sample gives an indication of the efficiency of this information capture; less diversity implies greater redundancy of the genetic information. Here, we propose a method to maximize genetic diversity within the selected samples. Our method is applicable to general experimental designs and robust to common problems such as missing data and dominant markers. In particular, we discuss its application to multi-parent advanced generation intercross (MAGIC) populations, where, although thousands of lines may be genotyped as a large population resource, only hundreds may need to be phenotyped for individual studies. Through simulation, we compare our method to simple random sampling and the minimum moment aberration method. While the gain in power over simple random sampling for all tested methods is not large, our method results in a much more diverse sample of genotypes. This diversity can be applied to improve fine mapping resolution once a QTL region has been detected. Further, when applied to two wheat datasets from doubled haploid and MAGIC progeny, our method detects known QTL for small sample sizes where other methods fail.
Similar content being viewed by others
References
Ansari-Mahyari S, Berg P, Lund MS (2009) Fine mapping quantitative trait loci under selective phenotyping strategies based on linkage and linkage disequilibrium criteria. J Anim Breed Genet 126:443–454
Bowcock AM, Ruiz-Linares A, Tomfohrde J, Minch E, Kidd JR, Cavalli-Sforza LL (1994) High resolution of human evolutionary trees with polymorphic microsatellites. Nature 368:455–457
Broman KW, Wu H, Sen S, Churchill GA (2003) R/qtl: QTL mapping in experimental crosses. Bioinformatics 19:889–890
Cavanagh C, Morell M, Mackay I, Powell W (2008) From mutations to MAGIC: resources for gene discovery, validation and delivery in crop plants. Curr Opin Plant Biol 11:215–221
Cavanagh CR, Taylor J, Larroque O, Coombes N, Verbyla AP, Nath Z et al (2010) Sponge and dough bread making: genetic and phenotypic relationships with wheat quality traits. Theor Appl Genet 121:815–828
Cochran WG (1977) Sampling techniques, 3rd edn. Wiley, New York. ISBN 0-471-16240-X
Coombes NE (2002) The reactive tabu search for efficient correlated experimental designs. Ph.D. thesis, Liverpool John Moores University, Liverpool
Franco J, Crossa J, Warburton ML, Taba S (2006) Sampling strategies for conserving maize diversity when forming core subsets using genetic markers. Crop Sci 46:854–864
Gagneur J, Elze MC, Tresch A (2011) Selective phenotyping, entropy reduction, and the Mastermind game. BMC Bioinform 12:406
Huang BE, George AW (2009) Look before you leap: a new approach to mapping QTL. Theor Appl Genet 119:899–911
Huang BE, George AW (2011) R/mpMap: a computational platform for the genetic analysis of multi-parent recombinant inbred lines. Bioinformatics 27:727–729
Huang BE, Cavanagh C, Rampling L, Kilian A, George AW (2012a) iDArTs: increasing the value of genomic resources at no cost. Mol Breed. doi:10.1007/s11032-011-9676-5
Huang BE, George AW, Forrest KL, Kilian A, Hayden MJ, Morell MK, Cavanagh CR (2012b) A multiparent advanced generation inter-cross population for genetic analysis in wheat. Plant Biotechnol J. doi:10.1111/j.1467-7652.2012.00702.x
Jaccard P (1908) Nouvelles recherches sur la distribution florale. Bul Soc Vaudoise Sci Natl 44:223–270
Jannink J-L (2005) Selective phenotyping to accurately map quantitative trait loci. Crop Sci 45:901–908
Jin C, Lan H, Attie AD, Churchill GA, Bulutuglo D, Yandell BS (2004) Selective phenotyping for increased efficiency in genetic mapping studies. Genetics 168:2285–2293
Kaufman L, Rousseeuw PJ (1990) Finding groups in data: an introduction to cluster analysis. Wiley, New York
Keyes GJ, Paolillo DJ, Sorrells ME (1989) The effects of dwarfing genes Rht1 and Rht2 on cellular dimensions and rate of leaf elongation in wheat. Ann Bot 64:683–690
Kover PX, Valdar W, Trakalo J, Scarcelli N, Ehrenreich IM, Purugganan MC et al (2009) A multiparent advanced generation inter-cross to fine map quantitative traits in Arabidopsis thaliana. PLoS Genet 5:e1000551
Manichaikul A, Dupuis J, Sen S, Broman KW (2006) Poor performance of bootstrap confidence intervals for the location of a quantitative trait locus. Genetics 174:481–489
Mohammadi SA, Prasanna BM (2003) Analysis of genetic diversity in crop plants—salient statistical tools and considerations. Crop Sci 43:1235–1248
Nyquist WE (1962) Differential fertilization in the inheritance of stem rust resistance in hybrids involving a common wheat strain derived from Triticum timopheevii. Genetics 47:1109–1124
Reif JC, Melchinger AE, Frisch M (2005) Genetical and mathematical properties of similarity and dissimilarity coefficients applied in plant breeding and seed bank management. Crop Sci 45:1–7
R Development Core Team (2011) R: A language and environment for statistical computing R foundation for statistical computing, Vienna, URL http://www.R-project.org, ISBN 3-900051-07-0
Smith AB, Liw P, Cullis BR (2006) The design and analysis of multi-phase plant breeding experiments. J Agric Sci 144:393–409
Sneath PHA, Snokal RR (1973) Numerical taxonomy. Freeman, San Francisco
The Complex Trait Consortium (2004) The collaborative cross, a community resource for the genetic analysis of complex traits. Nat Genet 36:1133–1137
Tibshirani R, Walther G, Hastie T (2001) Estimating the number of clusters in a data set via the gap statistic. J Royal Statist Soc B 63:411–423
Ward JH Jr (1963) Hierarchical grouping to optimize an objective function. J Am Statist Assoc 58:235–244
Yu J, Holland JB, McMullen MD, Buckler ES (2008) Genetic design and statistical power of nested association mapping in maize. Genetics 178:539–551
Author information
Authors and Affiliations
Corresponding author
Additional information
Communicated by I. Mackay.
Electronic supplementary material
Below is the link to the electronic supplementary material.
Rights and permissions
About this article
Cite this article
Emma Huang, B., Clifford, D. & Cavanagh, C. Selecting subsets of genotyped experimental populations for phenotyping to maximize genetic diversity. Theor Appl Genet 126, 379–388 (2013). https://doi.org/10.1007/s00122-012-1986-4
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s00122-012-1986-4