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Curr Opin Plant Biol. 2015 Apr;24:110-8. doi: 10.1016/j.pbi.2015.02.010. Epub 2015 Mar 17.

From association to prediction: statistical methods for the dissection and selection of complex traits in plants.

Author information

1
University of Illinois, Department of Crop Sciences, Urbana, IL 61801, USA. Electronic address: alipka@illinois.edu.
2
Michigan State University, Department of Biochemistry and Molecular Biology, East Lansing, MI 48824, USA; Cornell University, Plant Breeding and Genetics Section, School of Integrative Plant Science, Ithaca, NY 14853, USA.
3
University of Illinois, Department of Crop Sciences, Urbana, IL 61801, USA.
4
Iowa State University, Department of Agronomy, Ames, IA 50011, USA.
5
University of Illinois, High Performance Biological Computing Group and the Carver Biotechnology Center, Urbana, IL 61801, USA.
6
United States Department of Agriculture (USDA) - Agricultural Research Service (ARS), Robert W. Holley Center for Agriculture and Health, Ithaca, NY 14853, USA.
7
Cornell University, Plant Breeding and Genetics Section, School of Integrative Plant Science, Ithaca, NY 14853, USA.

Abstract

Quantification of genotype-to-phenotype associations is central to many scientific investigations, yet the ability to obtain consistent results may be thwarted without appropriate statistical analyses. Models for association can consider confounding effects in the materials and complex genetic interactions. Selecting optimal models enables accurate evaluation of associations between marker loci and numerous phenotypes including gene expression. Significant improvements in QTL discovery via association mapping and acceleration of breeding cycles through genomic selection are two successful applications of models using genome-wide markers. Given recent advances in genotyping and phenotyping technologies, further refinement of these approaches is needed to model genetic architecture more accurately and run analyses in a computationally efficient manner, all while accounting for false positives and maximizing statistical power.

PMID:
25795170
DOI:
10.1016/j.pbi.2015.02.010
[Indexed for MEDLINE]

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