Location: Plant Science ResearchTitle: Joint-multiple family linkage analysis predicts within-family variation better than single-family analysis of the maize nested association mapping population
|OGUT, FUNDA - North Carolina State University|
|Holland, Jim - Jim|
|BIAN, YANG - North Carolina State University|
Submitted to: Heredity
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 11/26/2014
Publication Date: 6/1/2015
Citation: Ogut, F., Bradbury, P., Holland, J.B., Bian, Y. 2015. Joint-multiple family linkage analysis predicts within-family variation better than single-family analysis of the maize nested association mapping population. Heredity. 114(4):552-563.
Interpretive Summary: Collections of related plant families can be scanned independently or in a combined analysis to find genome regions containing genes affecting complex traits. We compared individual family analysis and combined joint family analysis to see which method provided better predictions of corn line traits. The combined analysis almost always gave a better prediction and is the recommended method.
Technical Abstract: Quantitative trait loci (QTL) mapping has been used to dissect the genetic architecture of a trait and predict phenotypes for marker-assisted selection. Many QTL mapping studies in plants have been limited to one biparental family population. Joint analysis of multiple biparental families offers an alternative approach to QTL mapping with a wider scope of inference. Joint multiple population analysis should have higher power to detect QTL shared among multiple families, but may have lower power to detect rare QTL. We compared prediction accuracy of single family and joint family QTL analysis methods with five-fold cross validation for six diverse traits using the maize nested association mapping (NAM) population, which comprises 25 biparental recombinant inbred families. Joint family QTL analysis had higher mean prediction accuracies than single family QTL analysis for all traits at most significance thresholds, and was always better at more stringent significance thresholds. Most robust QTL (detected in more than 50% of data samples) were restricted to one family and were often not detected at high frequency by joint family analysis, implying substantial genetic heterogeneity among families for complex traits in maize. The superior predictive ability of joint family QTL models despite important genetic differences among families suggests that joint family models capture sufficient smaller effect QTL that are shared across families to compensate for missing some rare large-effect QTL.