Prediction of QTL genotypes and trait phenotypes using FlexQTL™: A pedigree-based analysis approach

Abstract

Cite this article: Verma S, Whitaker VM. Prediction of QTL genotypes and trait phenotypes using FlexQTL™: A pedigree-based analysis approach. J Plant Biol Crop Res. 2018; 2: 1006. Abstract Predicting phenotypes and QTL genotypes is of great value to breeding programs, especially those that make hundreds of crosses every year. Determining parents to make cross combinations is a complex process, and a breeder will use all available information to make that determination. Likewise, any available predictions of performance or of QTL genotypes for seedlings can be used in selection. Several well-established statistical methodologies are utilized to predict phenotypes and breeding values using genome-wide markers [1-3], but predicting unknown QTL genotypes and phenotypes based on specific QTL is rarely reported. However, a Pedigree-Based Analysis (PBA) software called FlexQTL™ has the statistical capability to predict QTL genotypes and unknown trait phenotypes. Although the theoretical foundation of this approach was laid years ago [4,5], no application has been reported in the literature to our knowledge. Rather, FlexQTL™ has primarily been used for QTL discovery and validation in multi-parental, pedigree-connected populations. Yet PBA can be used to predict QTL genotypes (QQ, Qq, qq) and phenotypes for individuals having marker data only. The goal of predicting unknown phenotypes is the same for established genome-wide selection approaches and for the FlexQTL™ approach, but pedigree connectivity is at the core of the analysis, and locus-specific markers (as opposed to genome-wide markers) are utilized. In both approaches, datasets are divided into training populations with pheno-typic and marker data andtest populations with marker data only. Of course, a high degree of relatedness between training and test population is essential using either approach. Overview of an example breeding program In a typical strawberry breeding program, the pool of elite parents used to make crosses is updated rapidly according to industry and environmental needs. Because of its octoploid ge-nome, determining the genetic architecture underlying traits in cultivated strawberry is challenging. In addition, strawberry varieties are mostly asexually propagated, and maintaining a strawberry clone from first year field trail to its release is expensive. Every year in the University of Florida (UF) strawberry breeding program, about a hundred crosses are made in the anticipation of superior fruit quality and better resistance to diseases. It takes a year to obtain performance data on progeny and make informed crossesfor the next year. If the genetic po