Effect of population size and unbalanced data sets on QTL detection using genome-wide association mapping in barley breeding germplasm |
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Authors: | Hongyun?Wang Kevin?P?Smith Emily?Combs Tom?Blake Richard?D?Horsley Email author" target="_blank">Gary?J?MuehlbauerEmail author |
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Institution: | (1) Department of Agronomy and Plant Genetics, University of Minnesota, St. Paul, MN 55108, USA;(2) Department of Plant Sciences and Plant Pathology, Montana State University, Bozeman, MT 59717, USA;(3) Department of Plant Sciences, North Dakota State University, Fargo, ND 58108, USA; |
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Abstract: | Over the past two decades many quantitative trait loci (QTL) have been detected; however, very few have been incorporated
into breeding programs. The recent development of genome-wide association studies (GWAS) in plants provides the opportunity
to detect QTL in germplasm collections such as unstructured populations from breeding programs. The overall goal of the barley
Coordinated Agricultural Project was to conduct GWAS with the intent to couple QTL detection and breeding. The basic idea
is that breeding programs generate a vast amount of phenotypic data and combined with cheap genotyping it should be possible
to use GWAS to detect QTL that would be immediately accessible and used by breeding programs. There are several constraints
to using breeding program-derived phenotype data for conducting GWAS namely: limited population size and unbalanced data sets.
We chose the highly heritable trait heading date to study these two variables. We examined 766 spring barley breeding lines
(panel #1) grown in balanced trials and a subset of 384 spring barley breeding lines (panel #2) grown in balanced and unbalanced
trials. In panel #1, we detected three major QTL for heading date that have been detected in previous bi-parental mapping
studies. Simulation studies showed that population sizes greater than 384 individuals are required to consistently detect
QTL. We also showed that unbalanced data sets from panel #2 can be used to detect the three major QTL. However, unbalanced
data sets resulted in an increase in the false-positive rate. Interestingly, one-step analysis performed better than two-step
analysis in reducing the false-positive rate. The results of this work show that it is possible to use phenotypic data from
breeding programs to detect QTL, but that careful consideration of population size and experimental design are required. |
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