Multi-Trait Genome-Wide Association Studies Reveal Loci Associated with Maize Inflorescence and Leaf Architecture

Abstract

<jats:title>Abstract</jats:title> <jats:p>Maize inflorescence is a complex phenotype that involves the physical and developmental interplay of multiple traits. Given the evidence that genes could pleiotropically contribute to several of these traits, we used publicly available maize data to assess the ability of multivariate genome-wide association study (GWAS) approaches to identify pleiotropic quantitative trait loci (pQTL). Our analysis of 23 publicly available inflorescence and leaf-related traits in a diversity panel of n = 281 maize lines genotyped with 376,336 markers revealed that the two multivariate GWAS approaches we tested were capable of identifying pQTL in genomic regions coinciding with similar associations found in previous studies. We then conducted a parallel simulation study on the same individuals, where it was shown that multivariate GWAS approaches yielded a higher true-positive quantitative trait nucleotide (QTN) detection rate than comparable univariate approaches for all evaluated simulation settings except for when the correlated simulated traits had a heritability of 0.9. We therefore conclude that the implementation of state-of-the-art multivariate GWAS approaches is a useful tool for dissecting pleiotropy and their more widespread implementation could facilitate the discovery of genes and other biological mechanisms underlying maize inflorescence.</jats:p>

Improving Genomic Prediction for Seed Quality Traits in Oat (Avena sativa L.) Using Trait-Specific Relationship Matrices

“…In these cases, it may be appropriate to use a combination of dimension-reduction and variable selection methods to select relevant phenotypes or linear combinations of phenotypes. Methods like principal component analysis or factor analysis have been used extensively to cope with high-dimensional traits (Runcie and Mukherjee, 2013;Wang and Stephens, 2018;Carlson et al, 2019;Sakamoto et al, 2019;Yu et al, 2019;Campbell et al, 2020;Rice et al, 2020;Runcie et al, 2020). These approaches can be used to create derived traits that capture (co)variance in the original data, and marker effects can be easily estimated using GWAS or whole-genome regression approaches.…”

Unraveling the Genetic Architecture of Two Complex, Stomata-Related Drought-Responsive Traits by High-Throughput Physiological Phenotyping and GWAS in Cowpea (Vigna. Unguiculata L. Walp)

“…To increase the power of QTL detection, here a combination of univariate GWAS and multivariate GWAS were used. Compared with traditional GWAS or univariate GWAS, multivariate GWAS or multitrait GWAS showed higher statistical power to detect signals for complex or multiple traits, as has also been demonstrated in studies of the genomic region associated with seed fatty acid in oat and inflorescence and leaf architecture in maize (Carlson et al, 2019;Rice et al, 2020). Our results showed that eight SNPs were detected by both methods, and 10 SNPs were detected only by multivariate GWAS, which proved the greater power of this method and indicate that the 10 SNPs may represent pleiotropic quantitative trait loci for θ cri and K Tr .…”

The regulatory landscape of early maize inflorescence development

“…The gray track beneath the GWAS SNPs defines the MNase HS portion of the genome (tassel and ear combined), which was used to subset the SNP set Since this analysis was performed using the NAM population, we expect that the haplotype blocks will limit our ability to resolve causal SNPs, regardless of subsetting the functional genome. We also tested this approach in a small association panel, the 282 line Goodman-Buckler maize diversity panel [67], using GWAS results for TBN from [68]. For the subset of markers present in tassel MNase HS regions (n = 71,024), new False Discovery Rate (FDR)-adjusted P-values were calculated using the Benjamini and Hochberg [69] procedure.…”