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Influence of Sample Size on Precision of Genetic Correlations in Red Clover
Author(s) -
Xie C.,
Mosjidis J. A.
Publication year - 1999
Publication title -
crop science
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.76
H-Index - 147
eISSN - 1435-0653
pISSN - 0011-183X
DOI - 10.2135/cropsci1999.0011183x003900030041x
Subject(s) - biology , confidence interval , statistics , sample size determination , standard error , sampling (signal processing) , seedling , sampling error , bootstrapping (finance) , population , nonparametric statistics , observational error , mathematics , agronomy , econometrics , demography , computer science , filter (signal processing) , sociology , computer vision
Previous investigations of genetic correlation ( r G )in dicate that r G is sensitive to sampling error. A nonparametric bootstrap method provides an alternative for estimating standard error and confidence intervals. Using the bootstrap method in a half‐sib red clover ( Trifolium pratense L.) population, we evaluated the effect of sample sizes on the point estimate, its sampling error, and confidence interval of r G . We conducted separate analyses for varying numbers of replications, families, and plants per family using a seedling data set. Precision of r G was affected by sample sizes and traits measured. To achieve a reasonable standard error and a small confidence interval of r G , a minimum of 42 half‐sib families each having nine plants in four replications would be required. Although this study was limited to seedling traits in a greenhouse, similar results may occur when measuring other traits.