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Maize yield is mostly determined by its grain size. Although numerous quantitative trait loci (QTL) have been identified for kernel-related traits, the application of these QTL in breeding programs has been strongly hindered because the populations used for QTL mapping are often different from breeding populations. However, the effect of genetic background on the efficiency of QTL and the accuracy of trait genomic prediction has not been fully studied. Here, we used a set of reciprocal introgression lines (ILs) derived from 417F × 517F to evaluate how genetic background affects the detection of QTLassociated with kernel shape traits. A total of 51 QTL for kernel size were identified by chromosome segment lines (CSL) and genome-wide association studies (GWAS) methods. These were subsequently clustered into 13 common QTL based on their physical position, including 7 genetic-background-independent and 6 genetic-background-dependent QTL, respectively. Additionally, different digenic epistatic marker pairs were identified in the 417F and 517F ILs. Therefore, our results demonstrated that genetic background strongly affected not only the kernel size QTL mapping via CSL and GWAS but also the genomic prediction accuracy and epistatic detection, thereby enhancing our understanding of how genetic background affects the genetic dissection of grain size-related traits. [ABSTRACT FROM AUTHOR]

Over the past decade, the use of genome-wide association studies (GWAS) in plant breeding for discovery and validation of quantitative trait loci (QTL) has vastly increased. Successful deployment and transferability of these findings, however, have been limited. To increase the value of GWAS for plant breeding, experimental and methodological aspects must be addressed and refined. Population designs and statistical techniques are necessary to properly account for the effect of long-range linkage disequilibrium. Success of current methods has been restricted to the detection of common-variants with moderate additive effects; discovery of rare variants or QTL that depart from additivity has been elusive. Pleiotropy casts doubt on the cause-effect relationships between markers and multiple traits. Major criticisms of association studies center on reproducibility of results and the lack of transferability to other environments and populations. We also discuss population structure, phenotypic plasticity, epistasis, and genotype-by-environment interactions as they apply to GWAS. Perspectives are given for environment-dependent QTL, experimental settings, use of next-generation populations, and deployment of GWAS results for breeding applications and strategic exploitation of genotype-by-environment interactions. In summary, we present an overview of contemporary issues in identification and deployment of marker-trait associations and suggest future avenues of research towards new methods and new sources of data.

Effect of genetic background on detection of quantitative trait locus (QTL) governing salinity tolerance (ST) was studied using two sets of reciprocal introgression lines (ILs) derived from a cross between a moderately salinity tolerant japonica variety, Xiushui09 from China, and a drought tolerant but salinity susceptible indica breeding line, IR2061-520-6-9 from the Philippines. Salt toxicity symptoms (SST) on leaves, days to seedling survival (DSS), and sodium and potassium uptake by shoots were measured under salinity stress of 140 mmol/L of NaCl. A total of 47 QTLs, including 26 main-effect QTLs (M-QTLs) and 21 epistatic QTLs (E-QTLs), were identified from the two sets of reciprocal ILs. Among the 26 MQTLs, only four (15.4%) were shared in the reciprocal backgrounds while no shared E-QTLs were detected, indicating that ST QTLs, especially E-QTLs, were very specific to the genetic background. Further, 78.6% of the M-QTLs for SST and DSS identified in the reciprocal ILs were also detected in the recombinant inbred lines (RILs) from the same cross, which clearly brings out the background effect on ST QTL detection and its utilization in ST breeding. The detection of ILs with various levels of pyramiding of nonallelic M-QTL alleles for ST from Xiushui09 into IR2061-520-6-9 allowed us to further improve the ST in rice. [ABSTRACT FROM AUTHOR]