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

Alexander E. Lipka, Catherine B. Kandianis, Matthew E. Hudson, Jianming Yu, Jenny Drnevich, Peter J. Bradbury, Michael A. Gore

Research output: Contribution to journalReview articlepeer-review

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.

Original languageEnglish (US)
Pages (from-to)110-118
Number of pages9
JournalCurrent opinion in plant biology
Volume24
DOIs
StatePublished - Apr 1 2015

ASJC Scopus subject areas

  • Plant Science

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