Identifying the genomic regions responsible for complex traits is critical for understanding the mechanisms behind trait architecture and for using them in marker assisted selection programs. Our group has focused on novel methodological approaches for quantitative trait loci mapping. A key decision in genome-wide association studies is how to control for spurious associations caused by population structure and other forms of genetic relatedness. We compared sixteen different models to control for population structure (Gutierrez et al., 2011) concluding that models are trait and environment specific, but mixed models will capture the loci of major effect. We innovated in using cofactors in genome-wide association mapping to resolve finer mapping (von Zitzewick et al., 2011; Locatelli et al., 2013; Racedo et al., 2016; Rosas et al., 2018).
With a simple simulation strategy, we showed ascertainment bias from marker imputation without a reference genome on genome-wide association studies (Brandariz et al., 2016). Additionally, we used multi-environment multi-QTL mapping to deal with genotype by environment interaction (Locatelli et al., 2013; Quero et al., 2014; Gutierrez et al., 2015). These projects provide a solid understanding of the genetic basis of complex traits and represent a useful framework for continuing efforts in that research line. In barley, our group found genomic regions responsible for malting quality (Gutierrez et al., 2011), winter-hardiness (von Zitzewick et al. 2011), disease resistance (Gutierrez et al., 2015) and other agronomic traits (Locatelli et al., 2013). In rice, we found genomic regions responsible for grain quality (Quero et al., 2018) and disease resistance (Rosas et al., 2018). For predicting complex traits, our work has focused on handling genotype by environment interaction (Lado et al., 2016; Monteverde et al., 2018; Monteverde et al., 2019; González-Barrios et al., in prep.), the size and structure of the training populations (Berro et al., in review), selecting the best crosses (Lado et al., 2017), and optimizing multi-trait studies (Lado et al., 2018). Future work includes the mapping of panicle architecture in oats (USDA-Hatch, Gonzalez-Barrios et al., in prep.), epistatic disease resistance in rice (Rosas et al., in prep), and yield in naked barley (USDA-OREI, Massman et al., in prep.).