Nested association mapping reveals the genetic architecture of spike emergence and anthesis timing in intermediate wheatgrass

Abstract

Intermediate wheatgrass (Thinopyrum intermedium) is an outcrossing, cool season grass species currently undergoing direct domestication as a perennial grain crop. Though many traits are selection targets, understanding the genetic architecture of those important for local adaptation may accelerate the domestication process. Nested association mapping (NAM) has proven useful in dissecting the genetic control of agronomic traits many crop species, but its utility in primarily outcrossing, perennial species has yet to be demonstrated. Here we introduce an intermediate wheatgrass NAM developed by crossing ten phenotypically divergent donor parents to an adapted common parent in a reciprocal manner, yielding 1,168 F1 progeny from 10 families. Using genotyping by sequencing, we identified 8,003 SNP markers and developed a population-specific consensus genetic map with 3,144 markers across 21 linkage groups. Using both genomewide association mapping and linkage mapping combined across and within families, we characterize the genetic control of flowering time. In the analysis of two measures of maturity across four separate environments, we detected as many as 75 significant QTL, many of which correspond to the same regions in both analysis methods across 11 chromosomes. The results demonstrate a complex genetic control that is variable across years, locations, traits, and within families. The methods were effective at detecting previously identified QTL, as well as new QTL that align closely to the well-characterized flowering time orthologs from barley, including Ppd-H1 and Constans. Our results demonstrate the utility of the NAM for understanding the genetic control of flowering time and its potential for application to other traits of interest.

QTL for seed shattering and threshability in intermediate wheatgrass align closely with well‐studied orthologs from wheat, barley, and rice

“…We previously described a 1,168-individual-F 1 nested association mapping (NAM) population of IWG that was developed from 11 phenotypically divergent parents from Cycle 2 of the University of Minnesota breeding program (Yu et al, 2008;Altendorf et al, 2021). In this previous work, we used flowering time traits as proof of concept and demonstrated that this population and the described methods were effective at identifying both new and previously detected quantitative trait loci (QTL) that align closely with wellcharacterized flowering time orthologs from barley, notably Ppd-H1, Constans, and PhyB (Altendorf et al, 2021). Here, we map three traits that are major targets of selection in IWG breeding: brittle rachis, floret shattering, and threshability.…”

“…Parental selection and methods for developing the IWG NAM were previously described in detail in Altendorf et al (2021). Briefly, 10 phenotypically diverse donor genets were identified from Cycle 2 of the UMN breeding program and crossed to WGN59, which is a low-shattering genet, in a reciprocal manner.…”

“…DNA variant detection methods were previously described in Altendorf et al (2021). Genomic DNA was extracted from each genet using the BioSprint 96 Plant DNA Kit (QIAGEN) and 96-plex genotyping-by-sequencing (Elshire et al, 2011;Poland et al, 2012) libraries were developed using a twoenzyme (PstI and MspI), two-barcode approach described in Zhang et al (2016) and sequenced on Illumina HiSeq 2500.…”

“…Estimated marginal means were calculated for genets nested within families on response and transformed scales as applicable using the 'emmeans' package (Lenth, 2018). Broad-and narrow-sense heritabilities (H 2 and h 2 , respectively) were calculated as described in Altendorf et al (2021), across years separately for STP and TLI locations as these growing regions are targeted by separate breeding programs (Zhang et al, 2016;DeHaan et al, 2018). The drought conditions at TLI in 2018 confounded the variation in shattering and threshability, therefore heritability estimates were calculated from TLI 2017 data only.…”

Genetic Architecture and QTL Selection Response For Kernza Perennial Grain Domestication Traits

“…Furthermore, decreased sequencing cost has resulted in a wealth of genomic information for crop improvement including genetic maps (Kantarski et al 2016), a draft genome sequence (https://phytozomenext.jgi.doe.gov/info/Tintermedium_v2_1). These genomics resources have enabled genomewide association studies (GWAS) for agronomic traits including seed size (Zhang et al 2017;Larson et al 2019), flowering time (Altendorf et al 2021b), and grain yield components (Bajgain et al 2019;Larson et al 2019) that can be used to better understand and guide IWG breeding.…”