Multivariate Genome-wide Association Analyses Reveal the Genetic Basis of Seed Fatty Acid Composition in Oat (Avena sativaL.)

Abstract

34Oat (Avena sativa L.) has a high concentration of oils, comprised primarily of healthful 35 unsaturated oleic and linoleic fatty acids. To accelerate oat plant breeding efforts, we sought to 36 identify loci associated with variation in fatty acid composition, defined as the types and quantities 37 of fatty acids. We genotyped a panel of 500 oat cultivars with genotyping-by-sequencing and 38 measured the concentrations of ten fatty acids in these oat cultivars grown in two environments. 39Measurements of individual fatty acids were highly correlated across samples, consistent with fatty 40 acids participating in shared biosynthetic pathways. We leveraged these phenotypic correlations 41 in two multivariate genome-wide association study (GWAS) approaches. In the first analysis, we 42 fitted a multivariate linear mixed model for all ten fatty acids simultaneously while accounting for 43 population structure and relatedness among cultivars. In the second, we performed a univariate 44 association test for each principal component (PC) derived from a singular value decomposition 45 of the phenotypic data matrix. To aid interpretation of results from the multivariate analyses, we 46 also conducted univariate association tests for each trait. The multivariate mixed model approach 47 revealed the numerous beneficial effects of oat consumption on human health, from reduction in 58 cardiovascular diseases risk (Grundy et al. 2018) to cancer prevention (Meydani 2009). These 59 positive health effects are likely due to oat's unique nutritional profile, which differs markedly 60 from that of other cereals, notably in the complement of essential amino acids, fatty acids, β-61 glucan, and phenolic compounds (Butt et al. 2008). In particular, lipids account for as much as 62 18% of the oat grain (Halima et al. 2015). Moreover, these lipids are predominantly composed of 63 unsaturated fatty acids, rendering oats a healthful energy source in human and animal diets. In 64 response to the growing awareness of oat's health-promoting properties, nutritional quality has 65 become a key target for oat breeders. Maintaining and/or optimizing lipid composition is an 66 important component of these efforts (Valentine et al. 2011). 67 To date, researchers have primarily investigated the genetic basis of variation in fatty acid 68 composition by mapping quantitative trait loci (QTL) in biparental populations (Hizbai et al. 69 2012). However, the use of such QTL in marker-assisted selection (MAS) is likely only effective 70 if the parents in the mapping population exhibit trait variation and are closely related to lines in 71 the relevant breeding populations (Snowdon and Friedt 2004). A genome-wide association study 72 (GWAS) can mediate this limitation if the population in which the GWAS is conducted captures 73 the genetic variation present in the target breeding population(s) (Lipka et al. 2015). Indeed, a 74 GWAS can identify allelic diversity associated with trait variation in complex plant pedigrees 75 when both genotypic and phenoty...

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

“…This prior has thicker tails compared to the Normal density used in BRR, but will shrink small-effect markers toward zero much stronger than BRR. These frameworks provide a means to estimate marker effects for a range of traits with different genetic architectures, which is consistent with what has been reported for fatty acid traits in oat (Carlson et al, 2019) (Supplementary Figures 1-18).…”

“…The following protocol was used for all experiments that measured fatty acid phenotypes. The methods are described in detail in Campbell et al (2020) and Carlson et al (2019). Briefly, dehulled seeds were homogenized, and 100 mg of pulverized tissue was used to separate polar and non-polar compounds using a biphasic extraction method.…”

Functional phenomics and genetics of the root economics space in winter wheat using high-throughput phenotyping of respiration and architecture

“…Correlation network analyses have been widely used in biology and social sciences to capture causality and precursor/product relationship patterns in functional traits. Despite the elegance of this approach, only a few studies applied network theory to plant root traits (Poorter et al, 2014;Messier et al, 2017;Carlson et al, 2019;Kleyer et al, 2019). In addition to root dry weight, SRL, and average diameter, SRR_M, which is rarely used in functional trait analysis, was identified as one of the hub traits and had substantial effects on the plant phenotype as a whole.…”

“…Network analysis with a Gaussian graphical model is more likely to capture causality and precursor/product relationships in trait networks relative to standard correlation analyses (Krumsiek et al, 2011;Carlson et al, 2019). The network analysis and the visualization of trait relationships were carried out with the R package 'qgraph' (Epskamp et al, 2012).…”

Heritable temporal gene expression patterns correlate with metabolomic seed content in developing hexaploid oat seed

“…After harvest, mature seeds were dehulled and analysed with gas chromatography–mass spectrometry (GC‐MS) and liquid chromatography–mass spectrometry (LC‐MS) at the Proteomics and Metabolomics Facility at Colorado State University following Carlson et al ().…”