Population structure analysis and genome-wide association study of a hexaploid oat landrace and cultivar collection

Abstract

Introduction
Oat (Avena sativa L.) is an important cereal crop grown worldwide for grain and forage, owing to its high adaptability to diverse environments. However, the genetic and genomics research of oat is lagging behind that of other staple cereal crops.

Methods
In this study, a collection of 288 oat lines originating worldwide was evaluated using 2,213 single nucleotide polymorphism (SNP) markers obtained from an oat iSelect 6K-beadchip array to study its genetic diversity, population structure, and linkage disequilibrium (LD) as well as the genotype–phenotype association for hullessness and lemma color.

Results
The average gene diversity and polymorphic information content (PIC) were 0.324 and 0.262, respectively. The first three principal components (PCs) accounted for 30.33% of the genetic variation, indicating that the population structure of this panel of oat lines was stronger than that reported in most previous studies. In addition, accessions could be classified into two subpopulations using a Bayesian clustering approach, and the clustering pattern of accessions was closely associated with their region of origin. Additionally, evaluation of LD decay using 2,143 mapped markers revealed that the intrachromosomal whole-genome LD decayed rapidly to a critical r² value of 0.156 for marker pairs separated by a genetic distance of 1.41 cM. Genome-wide association study (GWAS) detected six significant associations with the hullessness trait. Four of these six markers were located on the Mrg21 linkage group between 194.0 and 205.7 cM, while the other two significant markers mapped to Mrg05 and Mrg09. Three significant SNPs, showing strong association with lemma color, were located on linkage groups Mrg17, Mrg18, and Mrg20.

Discussion
Our results discerned relevant patterns of genetic diversity, population structure, and LD among members of a worldwide collection of oat landraces and cultivars proposed to be ‘typical’ of the Qinghai-Tibetan Plateau. These results have important implications for further studies on association mapping and practical breeding in high-altitude oat.

Full text

Population structure analysis and
genome-wide association study
of a hexaploid oat landrace and
cultivar collection
Lei Wang
1,2,3
, Jinqing Xu
1,2,3
, Handong Wang
1,2,3
,
Tongrui Chen
1,4
, En You
1,4
, Haiyan Bian
1,2
, Wenjie Chen
1,2,3,5
,
Bo Zhang
1,2,3,5
and Yuhu Shen
1,2,3,5
*
1
Key Laboratory of Adaptation and Evolution of Plateau Biota, Northwest Institute of Plateau Biology,
Chinese Academy of Sciences, Xining, China,
2
Qinghai Provincial Key Laboratory of Crop Molecular
Breeding, Northwest Institute of Plateau Biology, Chinese Academy of Sciences, Xining, China,
3
Laboratory for Research and Utilization of Qinghai Tibetan Plateau Germplasm Resources,
Northwest Institute of Plateau Biology, Chinese Academy of Sciences, Xining, China,
4
College of Life
Sciences, University of Chinese Academy of Sciences, Beijing, China,
5
Innovation Academy for Seed
Design, Chinese Academy of Sciences, Xining, China
Introduction: Oat (Avena sativa L.) is an important cereal crop grown worldwide
for grain and forage, owing to its high adaptability to diverse environments.
However, the genetic and genomics research of oat is lagging behind that of
other staple cereal crops.
Methods: In this study, a collection of 288 oat lines originating worldwide was
evaluated using 2,213 single nucleotide polymorphism (SNP) markers obtained
from an oat iSelect 6K-beadchip array to study its genetic diversity, population
structure, and linkage disequilibrium (LD) as well as the genotype–phenotype
association for hullessness and lemma color.
Results: The average gene diversity and polymorphic information content (PIC)
were 0.324 and 0.262, respectively. The first three principal components (PCs)
accounted for 30.33% of the genetic variation, indicating that the population
structure of this panel of oat lines was stronger than that reported in most
previous studies. In addition, accessions could be classified into two
subpopulations using a Bayesian clustering approach, and the clustering
pattern of accessions was closely associated with their region of origin.
Additionally, evaluation of LD decay using 2,143 mapped markers revealed that
the intrachromosomal whole-genome LD decayed rapidly to a critical r
2
value of
0.156 for marker pairs separated by a genetic distance of 1.41 cM. Genome-wide
association study (GWAS) detected six significant associations with the
hullessness trait. Four of these six markers were located on the Mrg21 linkage
group between 194.0 and 205.7 cM, while the other two significant markers
mapped to Mrg05 and Mrg09. Three significant SNPs, showing strong
association with lemma color, were located on linkage groups Mrg17, Mrg18,
and Mrg20.
Discussion: Our results discerned relevant patterns of genetic diversity,
population structure, and LD among members of a worldwide collection of
Frontiers in Plant Science frontiersin.org01
OPEN ACCESS
EDITED BY
Hongxiang Ma,
Yangzhou University, China
REVIEWED BY
Zujun Yang,
University of Electronic Science and
Technology of China, China
Ana Nikolic
´,
Maize research Institute Zemun Polje,
Serbia
*CORRESPONDENCE
Yuhu Shen
shenyuhu@nwipb.cas.cn
SPECIALTY SECTION
This article was submitted to
Plant Breeding,
a section of the journal
Frontiers in Plant Science
RECEIVED 26 December 2022
ACCEPTED 22 February 2023
PUBLISHED 21 March 2023
CITATION
Wang L, Xu J, Wang H, Chen T, You E,
Bian H, Chen W, Zhang B and Shen Y
(2023) Population structure analysis and
genome-wide association study of a
hexaploid oat landrace and
cultivar collection.
Front. Plant Sci. 14:1131751.
doi: 10.3389/fpls.2023.1131751
COPYRIGHT
©2023Wang,Xu,Wang,Chen,You,Bian,
Chen, Zhang and Shen. This is an open-
access article distributed under the terms o f
the Creative Commons Attribution License
(CC BY). The use, distribution or
reproduction in other forums is permitted,
provided the original author(s) and the
copyright owner(s) are credited and that
the original publication in this journal is
cited, in accordance with accepted
academic practice. No use, distribution or
reproduction is permitted which does not
comply with these terms.
TYPE Original Research
PUBLISHED 21 March 2023
DOI 10.3389/fpls.2023.1131751

oat landraces and cultivars proposed to be ‘typical’of the Qinghai-Tibetan
Plateau. These results have important implications for further studies on
association mapping and practical breeding in high-altitude oat.
KEYWORDS
hexaploid oat, population structure, linkage disequilibrium (LD), genome-wide
association analysis (GWAS), hullessness, lemma color
1 Introduction
Oat (Avena sativa L., 2n = 6x = 42) is an important cereal crop
originating from the Mediterranean region (Montilla-Bascon et al.,
2013), and an allohexaploid comprising three distinct subgenomes
(A, C, and D) that arose through cycles of interspecific
hybridization and polyploidization (Yan et al., 2016a). Oat is
well-adapted to a cool climate (Hoffman, 1995), and is grown
mostly in temperate regions of the world under a wide range of
environmental conditions for food, feed, and forage. To date, oat
has received considerable attention owing to its high nutritional
value and the ability to reduce blood cholesterol levels and mediate
the risk of cardiovascular disease (Anderson et al., 2009;Othman
et al., 2011).
Compared with other staple cereal crops such as rice, maize,
and wheat, the breeding, genomics, and population structure
analyses of oat have been lagging, primarily owing to its large,
repeat-rich, and polyploid genome and low investment (Tinker
et al., 2009;Yan et al., 2016b). Advances in molecular marker
exploitation technology have enhanced genome-wide marker
discovery in oat. Valuable studies have been carried out on oat
genetic diversity, population structure, quantitative trait locus
(QTL) identification, and genotype–phenotype association using
variousmolecularmarkers,includingamplified length fragment
polymorphism (ALFP) (Achleitner et al., 2008), random amplified
polymorphic DNA (RAPD) (Ruwali et al., 2013), simple sequence
repeat (SSR) (Montilla-Bascon et al., 2013), diversity arrays
technology (DArT) (Tinker et al., 2009;He and Bjørnstad,
2012), and single nucleotide polymorphisms (SNPs) (Winkler
et al., 2016;Cömertpay et al., 2018;Yan et al., 2020). A dense
consensus map of oat with 12,000 markers based on 12 biparental
populations was recently constructed (Chaffin et al., 2016)and
supplemented by high-density SNPs discovered through
genotyping-by-sequencing (GBS) (Huang et al., 2014;Bekele
et al., 2020). The availability of dense markers opens new
opportunities for association mapping, molecular breeding,
genetic diversity analysis, genome sequencing, and map-based
cloning in oat (Chaffin et al., 2016). Moreover, great progress has
been recently made in oat genome sequencing and assembly. Four
chromosome-scale genome assemblies of diploid, tetraploid, and
hexaploid oat have recently been reported (Maughan et al., 2019;
Li et al., 2021;Peng et al., 2022;https://wheat.pw.usda.gov/GG3/
graingenes_downloads/oat-ot3098-pepsico). These reference
genomes will accelerate the studies on oat evolution and
gene identification.
Studies show that population structure in oat is not as strong as
that in other crops (Winkler et al., 2016;Peng et al., 2022). No one
factor, such as geographical origin or morphological traits (such as
hulled or hulless grains, lemma color, and panicle type),
significantly affect population stratification patterns (Montilla-
Bascon et al., 2013;Esvelt Klos et al., 2016).
With the advent of rapid genotyping and next-generation
sequencing technologies, genome-wide association study (GWAS)
has emerged as a powerful routine strategy to identify genes or
regions affecting complex traits in crop species (e.g., Huang et al.,
2010, for rice agronomic traits; Wang et al., 2012, for resistance to
head smut in maize; Alqudah et al., 2014, for photoperiod response
in barley) over the last decade. In oat, GWAS has been performed to
study agronomic traits (Winkler et al., 2016;Tumino et al., 2017),
quality traits (Newell et al., 2012;Asoro et al., 2013;Carlson et al.,
2019), and biotic or abiotic stress tolerance (Tumino et al., 2016).
Six significant associations for lodging and two for plant height were
detected by Tumino et al. (2017) in a European oat collection using
the 6K SNP array. Three independent markers were significantly
associated with b-glucan concentration, and one showed sequence
homology to genes in rice (Newell et al., 2012). All of these studies
indicated that GWAS was an effective method for QTL detection
in oat.
The objectives of the present study were to (1) assess the genetic
diversity of an oat collection originating from globally diverse
regions; (2) characterize the population structure of the oat
germplasm; (3) evaluate the extent of pairwise linkage
disequilibrium (LD); and (4) perform GWAS for studying
morphological traits. The results of this study would be useful for
a deeper understanding and better management of the different
kinds of oat genetic resources. This study provides valuable genetic
markers for oat breeding programs, and represents a successful
example for further association studies in oat.
2 Materials and methods
2.1 Plant material
A collection of 288 oat landraces and cultivars was used in this
study. Out of 288 oat accessions, 257 accessions (199 landraces and
Wang et al. 10.3389/fpls.2023.1131751
Frontiers in Plant Science frontiersin.org02

58 cultivars), originating from 34 countries, were obtained from the
USDA National Small Grain Collection (NSGC) (Figure S1 and
Table S1). In addition, 29 commercial oat cultivars and two
mutagenized genotypes were collected from the oat-producing
provinces of northern China (Inner Mongolia, Hebei, Qinghai,
Gansu, and Jilin). Further details of the improvement status,
country of origin, growth habit, hull type (hulled or hulless), and
lemma color of these accessions are provided in Table S1. The
‘country of origin’information was used to assign each accession to
aregionoforiginasdefined by the United Nations
Statistics Division.
2.2 Genotypes
Genomic DNA of each accession was extracted from bulked leaf
samples of 2-week-old seedlings using the Plant Genomic DNA Kit
(Qiagen Inc., USA). The concentration and quality of each DNA
sample were assessed by agarose gel electrophoresis and with a
nanophotometer (NanoDrop 2000C, Thermo Scientific, USA). A
total of 4,852 SNP markers were assayed using the oat iSelect 6K-
beadchip array (Illumina, San Diego, CA, USA) at the USDA-ARS
Genotyping Laboratory at Fargo, ND, USA, as described by Tinker
et al. (2014). SNP genotype calls were made and adjusted in
GenomeStudio v2011.1 (Illumina, San Diego, CA, USA). The SNP
filtering process was performed according to the requirements of
the bioinformatics analysis. The following were eliminated:
multiallelic and monomorphic SNPs; SNPs with poor genotype
calls resulting from weak signal or ambiguous clustering; and SNPs
with relative minor allele frequency (MAF) ≤0.05 and missing
data > 0.1. The position information of SNPs used in the present
study was obtained from the consensus map of oat (version 3.1;
Chaffin et al., 2016). The consensus map contains 21 linkage groups,
scaled by genetic distance (cM). Linkage groups that are the
consensus of the underlying component maps are designated by
Merge (Mrg) and are reffered as Mrg01 to Mrg33.
2.3 Morphological trait data and
phenotypic analysis
Morphological trait data, including hullessness and lemma
color, were downloaded from the Germplasm Resources
Information Network (GRIN; https://npgsweb.ars-grin.gov/
gringlobal/search) on November 20, 2020, and the traits were
affirmed by field planting in the summer of 2020. Oat accessions
were planted in April at Diyao Village, Huangzhong County,
Qinghai Province (N 36°29′03.63″, E 101°31′09.91″). Each
accession was sowed two rows at a sowing density of 20 grains
per row. Rows were seperated from each other by 20 cm. At
maturity period, the hullessness trait of oat accessions was
recorded. If the caryopsis of an oat accession is tightly
surrounded by thick, lignin-rich hull after handy threshing, the
accession is reffered to as hulled oat and recorded as “Hulled”;
whereas if the hull of oat accession is papery and free-threshing, the
accession is reffered to as hulless oat and recorded as “Hulless”.At
milk-ripe stage, the lemma color of oat accessions is observed and
the color is recorded as “Amber/White”,“Black”,“Grey”,
“Red”,“Yellow”.
2.4 Genetic diversity, population structure,
and LD analyses
Statistics including genetic diversity and polymorphic
information content (PIC) were calculated for each locus using
the PowerMarker v3.25 software (Liu and Muse, 2005). To estimate
the population structure, three methodologies were compared.
Model-based structure analysis was performed using
STRUCTURE (Pritchard et al., 2000)withthenumberof
ancestral populations (K) ranging from 2 to 10, and the number
of subgroups was identified. Principal component analysis (PCA)
was carried out using the GCTA software (Yang et al., 2011), and
the percentage of genotypic variation explained by the first three
PCs is shown in section 3.2 to enable comparison with the data
obtained in previous oat studies. In addition, a neighbor-joining
tree was constructed using MEGA6 (Tamura et al., 2013) with 1,000
bootstrap replicates.
Pairwise LD was estimated using squared allele frequency
correlation (r
2
) based on loci that have been mapped on the
consensus map. The r
2
values were calculated using the LDcorSV
package of R (Mangin et al., 2012). The genome-wide and
chromosomal LD decay data were plotted against the genetic
distance (cM), and the LOESS curve was fitted using R.
2.5 GWAS
To evaluate genotype–phenotype associations, GWAS was
performed using a mixed linear model (MLM) implemented in
TASSEL v5 (Bradbury et al., 2007), with default settings. The PCA
matrix and kinship information (K matrix), generated using GCTA
and TASSEL v5, respectively, were incorporated in the MLM as
covariates. Quantile-quantile (Q-Q) plots and Manhattan plots
were generated using the qqman package of R. According with
the SNP annotations provided by Tinker et al. (2014), genes
orthologous to those carrying the trait-associated SNPs are
detected. The design sequence of significant associated SNP are
aligned to the new reference genome assembly of hulless common
oat (Peng et al., 2022) and the position of trait-associated SNPs in
the common oat physical map are determined. The physical
chromosomes are nominated as A1-A7, B1-B7, D1- D7.
3 Results
3.1 Genetic diversity
A total of 3,313 polymorphic SNPs were obtained using the 6K
Illumina platform. After filtering, 2,213 SNPs with no more than
10% missing calls and at least 5% MAF were retained, of which
2,143 SNPs were mapped on to the consensus map across all 21
Wang et al. 10.3389/fpls.2023.1131751
Frontiers in Plant Science frontiersin.org03

linkage groups, covering a total genetic distance of 2688.3 cM with
an average spacing of 1.25 cM between two SNPs. The number of
SNPs within and among linkage groups varied from 42 (on linkage
group Mrg19) to 211 (Mrg01) (Table 1). These markers showed an
average genetic diversity of 0.324, with a mode ranging from 0.096
to 0.50. The PIC value varied from 0.091 to 0.375 (average PIC =
0.262). Mean genetic diversity and PIC values, calculated for each of
the 21 chromosomes, were found to be similar within the
chromosomes (Table 1). The average PIC of oat landraces was
0.260, and that of oat cultivars was 0.259. There was no significant
difference between landrace and cultivar of oat.
3.2 Population structure
Population structure in the oat germplasm was investigated using
the model-based method implemented in the STRUCTURE software,
which assigns each individual a membership coefficient for each
cluster. Following the method of Evanno et al. (2005),theoptimal
number of populations (K) was estimated using the results exported
from STRUCTURE. The maximum delta K (DK) value was inferred
to be two, suggesting that K=2 was the most likely value for the oat
collection, with K = 3 being the second best(Figure S2). Accessions
with the probability of membership to either population greater than
0.7 were assigned to that specific population, and those with
membership probability less than 0.7 were considered admixtures.
According to these criteria, 239 of 288 accessions (82.99%) were
assigned to one of the two populations (POP1 and POP2), while the
remaining 49 accessions (17.01%) were retained in the admixed
group (Admixed) (Figure 1A,Table 2). Among the 288 accessions,
56 hulled accessions (48 landraces and 8 cultivars) were assigned to
POP1. The landraces in POP1 mainly originated from Western Asia
(27), Southern Europe (10), and Southern Asia (6). POP2 consisted of
130 hulled landraces, 45 hulled cultivars, and 8 hulless cultivars. The
landraces in POP2 were mainly from Eastern Asia (24), Eastern
Europe (49), Southern Europe, (32) and Southern America (11). The
cultivars in POP2 were principally from Eastern Asia (29), Northern
America (10), and Eastern Europe (8). Among the 31 cultivars
collected from North China, 20 including 8 hulless lines were
classified into POP2, while the remaining 9 cultivars were
considered admixed. Using K = 3, the population POP2 was
further divided into two subpopulations; however, the majority of
POP2 accessions, especially cultivars, fell into the admixed group
(Figure 1B,Table 2).
TABLE 1 SNP marker distribution and coverage, gene diversity, and polymorphic information content (PIC) across all linkage groups in 288 oat accessions.
Linkage group No. of SNP markers Chromosome length
(cM)
Marker coverage Gene diversity PIC
Mrg1 211 143.3 0.68 0.3040 0.2484
Mrg2 143 111.7 0.78 0.3048 0.2497
Mrg3 110 167.8 1.53 0.3391 0.2727
Mrg4 78 61.1 0.78 0.3517 0.2829
Mrg5 88 154.5 1.76 0.3556 0.2828
Mrg6 98 146.8 1.50 0.3236 0.2625
Mrg8 136 186.4 1.37 0.2955 0.2443
Mrg9 95 129 1.36 0.3383 0.2730
Mrg11 144 108.6 0.75 0.3212 0.2616
Mrg12 92 107.9 1.17 0.3127 0.2554
Mrg13 89 119.2 1.34 0.3418 0.2753
Mrg15 77 88.2 1.15 0.3221 0.2606
Mrg17 119 114.5 0.96 0.3422 0.2755
Mrg18 64 90 1.41 0.3223 0.2618
Mrg19 42 78.5 1.87 0.2983 0.2450
Mrg20 151 251.1 1.66 0.3430 0.2758
Mrg21 116 212.1 1.83 0.3130 0.2545
Mrg23 52 101.9 1.96 0.3366 0.2692
Mrg24 121 95.3 0.79 0.3408 0.2743
Mrg28 63 95.6 1.52 0.3242 0.2628
Mrg33 54 124.8 2.31 0.3378 0.2724
Total 2143 2688.3 1.25 0.3271 0.2648
Wang et al. 10.3389/fpls.2023.1131751
Frontiers in Plant Science frontiersin.org04

Next, we analyzed the correlation of the membership
coefficients of accessions with their region of origin, improvement
status, year of receipt, and lemma color. The correlation coefficient
was highest between membership coefficients and origin regions
(0.50, P< 0.001), and this correlation in landraces was up to 0.55
(P< 0.001) (Table S2). We also analyzed the distribution of
landraces in the two populations (Figure 2,Table 2). All landraces
from Eastern Asia, the overwhelming majority of landraces from
TABLE 2 Grouping of 288 oat accessions based on STRUCTURE analysis at K = 2 and K = 3.
Region of origin POP1
a
POP2
b
Total POP1 POP2 POP3 Total
Land. Cult. Land. Cult. Land. Cult. Land. Cult. Land. Cult.
Eastern Asia 24 29 53 19 2 3 9 33
Western Asia 27 11 38 26 11 37
Southern Asia 6 2 8 6 2 1 9
Central Asia 1 1 1 1
Eastern Europe 1 2 49 8 60 1 2 34 3 12 3 55
Western Europe 1 1 2 1 1 2
Southern Europe 10 32 42 10 32 1 43
Northern Europe 3 3 2 1 3
South America 3 11 14 3 2 6 11
Northern America 3 10 13 3 4 3 10
Eastern Africa 1 1 1 1
Northern Africa 1 1 0
Oceania 2 1 3 2 1 3
Total 48 8 130 53 239 47 8 101 12 23 17 208
a
POP1, population 1; Land., number of landraces; Cult., number of cultivars.
b
POP2, population 2.
A
B
FIGURE 1
Population structure of 288 oat (Avena sativa. L) accessions. (A, B) Population structure estimated using STRUCTURE and illustrated as bar plots (K =
2 and 3). Each accession is shown as a thin vertical segment, and the color indicates the proportion of each population. Each subgroup is
represented by an individual color.
Wang et al. 10.3389/fpls.2023.1131751
Frontiers in Plant Science frontiersin.org05

Eastern Europe (49 of 50), and a large part of landraces from
Southern Europe (32 of 42) and Southern America (11 of 14) were
assigned to POP2. Landraces from Western Asia were divided into
the two populations (27, POP1; 11, POP2). Most of the landraces
from Southern Asia (6 of 8) and a minority of accessions from
Southern Europe (10 of 42) were assigned to POP1. Overall, the
landrace accessions in POP1 were distributed in lower latitude
regions compared with those in POP2 (Figure 2).
PCA was also used to infer the population structure of the oat
germplasm. The first three principal components (PC1–PC3)
together accounted for 30.33% of the genetic marker variation
(PC1, 16.26%; PC2, 8.05%; PC3, 6.02%). Two two-dimensional
(2-D) scatter plots of the 288 oat genotypes (Figure 3) exhibited a
similar population stratification to that of STRUCTURE. PC1
clearly separated POP1 from POP2, and some accessions,
identified as admixed in STRUCTURE, were placed in
intermediate positions in the PCA plot (Figure 3).
The stratification of the oat germplasm was further determined
by the neighbor-joining (NJ) method implemented in the program
MEGA 6.0 (Figure 4), and the accessions were divided into three
clusters. Consistent with the results of STRUCTURE, most oat
accessions from Eastern Asia, Eastern Europe, and Southern
America grouped into Cluster 2 (Supplementary Table S3). Unlike
the STRUCTURE results, most oat lines from Western Asia grouped
into Cluster 1 (Table S3). Generally, the stratification of accessions
was associated with their geographical origin.
The PIC of POP1 (0.2621) was greater than that of POP2
(0.2348), indicating that POP1 was genetically more diverse than
POP2. Highly significant (P< 0.001) genetic variance resided
among the two populations (17.34%). The estimated fixation
index (Fst = 0.1379) was also highly significant (P<0.001).
According to the degree of population genetic variance
corresponding to Fst values (Hartl et al., 1997), there was
moderate genetic variance between the two populations.
A
B
FIGURE 3
Scatterplots showing the results of principal component analysis (PCA) conducted based on the SNP marker data of oat lines labeled according the
different subgroups identified by STRUCTURE (membership coefficient ≥0.7). (A, B) Population stratification of oat lines according to the first
component PC1 vs. PC2 (A), and PC1 vs. PC3 (B). Admixed individuals are indicated with ×.
FIGURE 2
Geographical distribution of oat landraces at K = 2. The locations of oat landraces, with population membership coefficient ≥0.7, are indicated by
different colors.
Wang et al. 10.3389/fpls.2023.1131751
Frontiers in Plant Science frontiersin.org06

3.3 LD analysis
The 2,143 mapped SNPs were used to explore the LD level in
the present oat panel. The r
2
values revealed a high degree of
association among many unlinked and loosely linked markers
within all chromosomes (Figure S3). A critical value of r
2
beyond
which LD is likely to be caused by genetic linkage was calculated by
resampling unlinked markers, and was fixed at 0.156 (Breseghello
and Sorrells, 2006). The r
2
values for intrachromosomal locus pairs
ranged from 0 to 1, with an average of 0.085. Of these r
2
values,
15.36% exceeded 0.156 and averaged to 0.365. The genome-wide
and intrachromosomal LD decayed rapidly with genetic distance
(Figure S4). The point at which the LOESS curve and the line r
2
=
0.156 intersected was considered the average LD decay distance.
Based on the criteria, the average genome-wide LD decay distance
was 1.41 cM, and the intrachromosomal LD decayed between 0.02
and 14.99 cM (Figure S4,Table S4). The different chromosomes
showed different LD levels, indicating that they had been subjected
to variable intensities of natural and artificial systematic selection.
3.4 Genome-wide association
GWAS was performed using 2,143 mapped SNPs. This number
of markers was used to establish the threshold of statistical
significance of association at p≤2.33 × 10
-5
,calculatedby
applying the Bonferroni correction with an experiment-wise a=
0.05. At p< 2.33 × 10
-5
, nine SNP markers showed significant
associationwiththegrainhulltypeandlemmacolorofoat
accessions, and explained 8.28–22.39% of the phenotypic
variation in these traits (Table 3;Table S5).
The strongest evidence of association with the hullessness trait was
observed on linkage group Mrg21, specifically based on two markers,
GMI_GBS_84661 and GMI_ES01_c8241_504, both located at 194 cM,
FIGURE 4
Neighbor-joining phylogenetic tree of 288 oat accessions based on 2,213 SNPs. Each accession is denoted as a vertical line in three colored
subclades corresponding to the three clusters.
Wang et al. 10.3389/fpls.2023.1131751
Frontiers in Plant Science frontiersin.org07

with the minimum p-value less than 1.0 × 10
-10
(Figures 5A,6A).
Strong associations for this trait were also found on linkage group
Mrg21 (GMI_GBS_67251 and GMI_ES22_c7478_431, comapping at
205.7 cM, p<1.0×10
-5
). Two additional markers, one mapped on
Mrg05 (GMI_ES02_lrc13788_346, p<1.0×10
-5
) and the other on
Mrg09 (GMI_ES17_c10594_472, p<1.0×10
-6
), were also significantly
associated with hullessness (Figure 5A,6A).
Three significant markers were identified for lemma color.
Among these, GMI_ES15_c2369_181 (Mrg20, 14.7 cM) showed
the most significant effect on lemma color (p<1.0×10
-10
)
A
B
FIGURE 5
Genome-wide association study (GWAS) of the hullessness and lemma color traits in the oat collection using 2,143 SNP markers. (A) Hullessness;
(B) lemma color. The red line represents the threshold calculated according to the false discovery rate (FDR). Markers above the red line in (A, B)
were significantly associated with the respective trait.
TABLE 3 Hullessness- and lemma color-associated SNPs and their positions in the oat consensus genetic map.
Trait SNP marker Linkage group Position (cM) P-value R
2
(%)
Hullessness GMI_GBS_84661 Mrg21 194 1.02E-11 22.39
GMI_ES01_c8241_504 Mrg21 194 2.13E-11 17.11
GMI_GBS_67251 Mrg21 205.7 8.02E-07 8.95
GMI_ES22_c7478_431 Mrg21 205.7 7.57E-06 8.36
GMI_ES02_lrc13788_346 Mrg5 131.6 6.10E-06 8.80
GMI_ES17_c10594_472 Mrg9 61.8 1.26E-05 8.28
Lemma Color GMI_ES15_c2369_181 Mrg20 14.7 4.86E-11 21.16
GMI_DS_oPt-18257_376 Mrg17 53.8 5.58E-08 12.68
GMI_GBS_13773 Mrg18 56 1.42E-06 9.86
Wang et al. 10.3389/fpls.2023.1131751
Frontiers in Plant Science frontiersin.org08

(Figures 5B,6B). The other two markers significantly associated
with lemma color were GMI_DS_oPt-18257_376 (Mrg17, 53.8 cM)
and GMI_GBS_13773 (Mrg18, 56 cM) (Figures 5B,6B).
4 Discussion
4.1 Patterns of genetic diversity and
population structure
Oat has not only been grown as a grain or forage crop but it has
also received significant attention as a whole-grain food owing to its
health benefits for humans (Newell et al., 2011). Approximately
80,000 oat cultivars and over 20,000 wild oat accessions have been
preserved in gene banks, and are considered as a pool of potentially
useful genes (Lipman et al., 2005). Genetic diversity and population
structure studies of the oat germplasm provide important
information for their genetic conservation and breeding
(Montilla-Bascon et al., 2013). The large number of available
molecular markers and the high-density consensus map make it
easier and more efficient for researchers to explore genetic diversity,
population structure, and phenotype-associated QTLs in a sample
of the oat germplasm.
In the present study, 2,213 high-quality polymorphic SNPs were
identified among 288 oat accessions using the 6K SNP array. The
reduction of genetic diversity in cereal crops, such as wheat and
maize, during domestication and modern breeding has been a
longstanding concern (Eyre-Walker et al., 1998;Cavanagh et al.,
2013;Beissinger et al., 2016). However, in some instances, the loss of
diversity was not observed from landraces to breeding accessions.
Reif et al. (2005) reported that wheat genetic diversity narrowed
down from 1950 to 1989 but was enhanced from 1990 to 1997. A
similar increase in the average genetic diversity was detected in oat
cultivars released between 1930 and 1950 (Fu et al., 2003). Oat
landraces and cultivars used in this study showed similar genetic
diversity, and a decreasing trend of genetic diversity was not
observed from landraces to cultivars. One plausible explanation
for this observation is that oat has a relatively short modern
breeding history compared with other main cereal crops, and
therefore has not experienced intense artificial selection (Yan
et al., 2020). Another main reason for the result is that breeders
used varied and geographically diverse oat resources during
A
B
FIGURE 6
Quantile-quantile (Q-Q) plots of the GWAS data of hullessness and lemma color using a mixed model, with the PCA matrix and kinship information
as covariates. (A) Hullessness; (B) lemma color.
Wang et al. 10.3389/fpls.2023.1131751
Frontiers in Plant Science frontiersin.org09

breeding programs. This extensive hybridization not only increased
oat yields but also, simultaneously, broadened the genetic
background of the cultivars (Fu et al., 2003;Yan et al., 2020). Oat
accessions from different regions exhibited varied genetic diversity.
Previous studies revealed that the genetic diversity of oat lines
originating in Europe was lower than that of lines originating in
Northern and Southern America (Fu et al., 2005;Achleitner et al.,
2008); our results showed a similar trend (Table S6). The superior
genetic diversity of oat lines from America was closely related to oat
domestication, spread, and breeding history. The oat accessions of
America were originally brought from Europe by humans. Later,
germplasm exchange of oat took place frequently, and more exotic
varieties (other species or ecotypes, even wild resources) originating
from worldwide locations were used in breeding programs (Rodgers
et al., 1983). The introgression of exogenous genes greatly increased
the genetic diversity of American oat accessions.
Strong population structure has been reported within the
germplasm of other crops. For example, Muñoz-Amatriaı

n et al.
(2014) separated barley accessions into distinct subgroups, based on
the row number (2 vs. 6), growth habit (spring vs. winter), hull type
(hulled vs. hulless), improvement status (wild, landrace, and
cultivar), and geographical origin. Oat also has four recognizable
characteristics, including hulled, hulless, spring, and winter.
Unfortunately, most previous studies reported the oat population
structure as weak, and could not use any morphological trait to
divide the oat accessions into distinct subpopulations (Montilla-
Bascon et al., 2013;Esvelt Klos et al., 2016;Winkler et al., 2016).
PCA revealed that the first three PCs accounted for 23.8% of the
genetic variation in a 635-member CORE panel of elite oat
germplasm (Esvelt Klos et al., 2016), and the first five PCs
explained 25.8% of the variation in an 805-member global panel
of oat lines (Yan et al., 2020). In these studies, the subgroups
revealed by PCA or model-based K-means clustering overlapped
and diffused. By contrast, in the USDA collection of 759 oat
landraces and historic cultivars, PC1–3 together explained 38.8%
of the marker variation, and the majority of oat lines clustered into
three subgroups. The population structure pattern was strongly
associated with the lemma color and geographical origin of oat lines
(Winkler et al., 2016). In the present study, PC1–3 accounted for
30.33% of the genetic variation, supporting a relatively distinct
population structure. The majority of 288 oat accessions were
divided into two subpopulations. The distribution of the two
subpopulations in the 2-D scatter plots was nonoverlapping, and
the relationship among oat lines within each subpopulation was
relatively tight (Figure 3). The strong association of population
structure with geographical origin was especially prominent among
landraces (Figure 2), echoing the findings of Fu et al. (2005) and
Winkler et al. (2016). Such a distribution pattern could probably be
explained by the domestication and spread of oat around the world.
It is widely accepted that oat originated in the Mediterranean
region, with Turkey as its center of genetic diversity (Loskutov,
2008). Oat was then brought to Europe and Asia (Newton et al.,
2011). Subsequently, cultivated oat accessions were introduced into
America by the Spanish and British explorers (Rodgers et al., 1983).
In the present study, POP1 mainly consisted of landraces from
Western Asia (Turkey) and its circumjacent regions (Southern Asia
and Southern Europe), while other landraces in POP2 were from
regions farther away from Western Asia (Eastern Asia, Eastern
Europe, and South America), which is concerning. The other point
of concern is that at K = 2, six of the fourteen hulless oat lines from
China were identified as admixtures, while the other eight hulless
lines clustered into POP2 together with the hulled landraces and
cultivars from China, even though their values of membership
probability were not high. At K = 6, all 14 hulless lines were
identified as admixtures (data not shown). In our study, all the
hulless oat lines were cultivars. Pedigree information suggests that
most Chinese hulless cultivars have been selected from crosses
between hulless and hulled accessions (Ren and Yang, 2018;Yan
et al., 2020). Therefore, the population differentiation between
Chinese hulless oat cultivars and Chinese hulled oat accessions
was dramatically weakened, and hulless cultivars showed a high
level of admixture or proximity to common oats (Yan et al., 2020).
4.2 GWAS for hullessness and lemma color
During genome-wide association analysis, it is necessary to
determine the density and coverage of markers according to the
extent of LD that affects the power and resolution of GWAS in a
given population. In the current study, the LD decay results
(genome-wide average LD = ~1.4 cM) suggested that at least one
marker per 1.4 cM would be necessary to perform effective GWAS
in the oat population, similar to previous studies (genome-wide
average LD = ~1.5 cM) (Newell et al., 2011;Yan et al., 2020). Given
that the total length of the oat consensus map estimated by Chaffin
et al. (2016) is 2,843 cM, the number of markers required for the oat
population used in this study was approximately 2,000. Therefore,
we performed GWAS using 2,143 polymorphic SNPs, which
surpassed the minimum number of SNPs required and
were sufficient.
Cultivated oats are generally classified as hulled and hullesstypes,
depending on their grain phenotype. The hull of a hulled oat variety is
thick, lignin-rich, and hard-to-remove, whereas that of a hulless
accession is papery-thin and free-threshing (Yan et al., 2020).
Previous studies demonstrated that the hulless trait in oat is
controlled by a single, incompletely dominant gene (N1) interacting
with modifying genes (Boland and Lawes, 1973). The N1 locus was
mapped by De Koeyer et al. (2004) to linkage group TM_5 (Terra ×
Marion), which was homologous to KO_24_26_34 and was later
afirmedto be located at approximately 200 cM on Mrg21 in the
consensus map (Chaffin et al., 2016). Ubert et al. (2017) mapped the
N1 locus in two recombinant inbred line (RIL) populations (UFRGS
01B7114-1-3 × UFRGS 006013-1 and URS Taura × UFRGS 017004-
2), and found that the SNP markers associated with the hulless trait
were located on the linkage group Mrg21 near marker
GMI_ES14_c19259_657 at 212 cM. The GWAS strategy was also
Wang et al. 10.3389/fpls.2023.1131751
Frontiers in Plant Science frontiersin.org10

employed to study the hulless trait of oat. Tumino et al. (2016) found
a robust association between the hulless trait at the178.3 cM position
on Mrg21. Another GWAS performed by Yan et al. (2020) found that
the most significant markers affecting the hulless trait were located on
Mrg21 at 205.3, 212.1, and 195.7 cM. The positions of associated
markers in the two GWAS were discrete. This could be due to the
small number of hulless oat lines in the mapping population used by
Tumino et al. (2016), which limited the power of GWAS. In the
present study, six significant SNPs were found to be associated with
the hulless trait of oat. Four of the six associated SNPs were located at
194.0 and 205.7 cM on Mrg21, suggesting a major QTL between these
genetic map positions (Table 3,Figure 6A). These SNPs were located
close to the N1 locus detected in the two RIL populations by Ubert
et al. (2017), and to the associated SNPs found by Yan et al. (2020).
Recently, Peng et al. (2022) generated a high-quality reference
genome assembly of hulless common oat (AACCDD genome), and
performed GWAS to identify the genomic loci contributing to the
hulless trait. A strong peak associated with the trait was detected at
the end of chromosome 4D, and colocalized with the N1 locus. We
mapped six SNPs associated with the hulless trait in common oat
(Table 4). Four of the six SNPs (GMI_GBS_84661, GMI_
ES01_c8241_504, GMI_GBS_67251 and GMI_ES22_c7478_431)
mapped to the end of chromosome 4D, and two of these four
SNPs (GMI_GBS_67251 and GMI_ES22_c7478_431) were located
in the candidate region of the N1 locus (Peng et al., 2022). These
results provide further evidence suggesting that the major locus N1
controls the hulless trait in oat. The formation of hulless grain is also
observed in other crops, such as barley (Hordeum vulgare.L)(Taketa
et al., 2008). Lemma and palea are attached firmly to the grain in
hulled barley, while they can be easily separated from the grain in
hulless barley. Most studies concluded that the hulled grain trait is
governed by a single locus (NUD) in barley, and hulless barley
varieties carry a loss-of-function nud allele. However, additional
loci significantly associated with hullessness have recently been
identified in barley through GWAS (Milner et al., 2019;Wabila
et al., 2019). In the present study, two additional markers
(GMI_ES02_lrc13788_346 and GMI_ES17_c10594_472), identified
on Mrg05 and Mrg09 for the first time, were also found to be
significantly associated with the hulless trait. Thus, our results suggest
that the hulless trait of oat is regulated not only by the N1 locus but
also by other genes, as speculated previously (De Koeyer et al., 2004).
The gene A.satnudSFS4D01G000045, annotated as a receptor-
like kinase, was suggested to be a promising candidate for the gene
controlling the hulled/hulless trait in oat (Peng et al., 2022). In
accordance with the SNP annotations provided by Tinker et al.
(2014), genes orthologous to those carrying the hullessness trait-
associated SNPs are listed in Table 4. Notably, the candidate gene
A.satnudSFS4D01G000045 was not present among these ortholgous
genes (Table 4). However, we found that the gene containing the
marker GMI_GBS_67251 at the N1 locus likely encodes 4-
TABLE 4 Annotated genes located near significant SNP markers in a recently released genome assembly of common oat.
Trait Locus Linkage
group
Position
(cM)
Gene ID
containing the
SNP
Protein ID Description Mapping details in
common oat
Chr. Nucleotide
position
(Mb)
Hullessness GMI_GBS_84661 Mrg21 194 LOC100844057
(Brachypodium
distachyon)
XP_003565107.1 cell division protein
FtsY homolog,
chloroplastic
4D 438.93
GMI_ES01_c8241_504 Mrg21 194 LOC100835760
(Brachypodium
distachyo)
XP_003565080.1 villin-2-like isoform
1
4D 435.07
4D 388.03
GMI_GBS_67251 Mrg21 205.7 LOC4341718 (Oryza
sativa L. subsp.
japoinca)
ACA09448.1 probable 4-
coumarate: CoA
ligase 4
4D 400.03
4D 449.44
GMI_ES02_lrc13788_346 Mrg5 131.6 LOC123429495
(Hordeum vulgare
subsp. vulgare)
BAK05479.1 predicted protein 6A 403.72
GMI_ES22_c7478_431 Mrg21 205.7 LOC100824236
(Brachypodium
distachyo)
XP_003559637.1 PHD finger protein
ING2
4D 449.02
4D 307.88
GMI_ES17_c10594_472 Mrg9 61.8 LOC100843646
(Brachypodium
distachyo)
XP_003566307.1 calmodulin binding
protein PICBP
1A 317.10
1A 454.91
Lemma
color
GMI_ES15_c2369_181 Mrg20 14.7 LOC780697 (Triticum
aestivum)
AAY84880.1 splicing factor U2af
large subunit B-like
4D 23.20
GMI_DS_oPt-18257_376 Mrg17 53.8 ––– 6C 137.94
GMI_GBS_13773 Mrg18 56 ––– 6C 138.18
Wang et al. 10.3389/fpls.2023.1131751
Frontiers in Plant Science frontiersin.org11

coumarate: CoA ligase 4 (4CL4) protein. The 4CL4 protein
participates in phenylpropanoid metabolism by mediating the
activation of a number of hydroxycinnamates for the biosynthesis
of monolignols and other phenolic secondary metabolites (Gui
et al., 2011). The rice homolog of 4CL4 (IRAL1/4CL4) is also
involved in lignin biosynthesis, and mutation of 4CL4 reduces the
lignin content of roots and leaves (Liu et al., 2020). Ubert et al.
(2017) speculated that the reduced lignification of lemma in hulless
oat maybe related to genes that regulate lignin composition and
biosynthesis. Therefore, we conjecture that 4CL4 might be another
candidate gene controlling the hulled/hulless trait in oat, although
more evidence is needed to verify this speculation. The annotated
gene of GMI_ES22_c7478_431, which was also located in the N1
locus region, was predicted to encode the PHD finger protein ING2,
which participates in the growth regulation biological process. Oat
possesses a large, repeat-rich polypoid genome that has undergone
extensive rearrangement. Once a better quality genome sequence of
hexaploid oat becomes available, additional candidate genesmust be
discovered and annotated.
The most significant SNP affecting lemma color
(GMI_ES15_c2369_181) in the present study was mapped to
Mrg20 (14.7 cM); this SNP was also identified by Tumino et al.
(2016) and Winkler et al. (2016) using GWAS. The SNP marker was
mapped to chromosome 7D in common oat (Table 4), and the gene
containing the marker shared similarity with the gene encoding the
splicing factor U2af large subunit B-like protein. Two other
significant markers, GMI_DS_oPt-18257_376 and
GMI_GBS_13773, were reported for the first time in this study.
Both these markers were located on chromosome 6C in close
proximity to each other. It is possible that this region on
chromosome 6C governs lemma color in oat. Lemma color is a
complex and difficult-to-interpret trait. Previous investigation of
the inheritance of black and gray colored lemma in a specific hybrid
population of oat revealed that black lemma color is controlled by
more than two loci (Hoffman, 1999). Studies conducted to date on
lemma color suggest that the expression of this trait is affected by
environmental factors and epistatic effects.
GWAS in our study found several SNPs associated with
hullessness and lemma color in the common oat and the results
provide the basis to explore the molecular mechanism of the traits
in the further research. The present stuty serves as a typical example
to explore genetic basis in other quality and quantity traits using
GWAS strategy in common oat.
Data availability statement
The data presented in the study are deposited in the the
Genome Sequence Archivein National in Genomics Data Center ,
China National Center for Bioinformation / Beijing Institute of
Genomics, Chinese Academy of Sciences (GSA: PRJCA015511,
https://ngdc.cncb.ac.cn/bioproject/browse/PRJCA015511) that are
publicly accessible at https://ngdc.cncb.ac.cn/gsa.
Author contributions
YS and LW conceived and designed the research. LW and JX
performed the experiments and analyzed the data. HW, HB, TC, EY
assisted in the experiments and edited the figures. LW wrote the
manuscript. YS, BZ, and WC revised the manuscript. All authors
contributed to the article and approved the submitted version.
Funding
This research was financially supported by the Second Tibetan
Plateau Scientific Expedition and Research (STEP) program (Grant
No. 2019QZKK0303), the Innovation team project of basic research
program of Qinghai province (2022-ZJ-902), and the “Western
Cross Team”key laboratory special project, Chinese Academy
of Sciences.
Acknowledgments
We thank the U.S. National Plant Germplasm System (NPGS)
for providing the oat germplasm.
Conflict of interest
The authors declare that the research was conducted in the
absence of any commercial or financial relationships that could be
construed as a potential conflict of interest.
Publisher’s note
All claims expressed in this article are solely those of the authors
and do not necessarily represent those of their affiliated organizations,
or those of the publisher, the editors and the reviewers. Any product
that may be evaluated in this article, or claim that may bemade by its
manufacturer, is not guaranteed or endorsed by the publisher.
Supplementary material
The Supplementary Material for this article can be found online
at: https://www.frontiersin.org/articles/10.3389/fpls.2023.1131751/
full#supplementary-material
Wang et al. 10.3389/fpls.2023.1131751
Frontiers in Plant Science frontiersin.org12

References
Achleitner, A., Tinker, N. A., Zechner, E., and Buerstmayr, H. (2008). Genetic diversity
among oat varieties of worldwide origin and associations of AFLP markers with quantitative
traits. Theor. Appl. Genet. 117 (7), 1041–1053. doi: 10.1007/s00122-008-0843-y
Alqudah, A. M., Sharma, R., Pasam, R. K., Graner, A., Kilian, B., and Schnurbusch, T.
(2014). Genetic dissection of photoperiod response based on GWAS of pre-anthesis
phase duration in spring barley. PloS One 9 (11), e113120. doi: 10.1371/
journal.pone.0113120
Anderson, J. W., Baird, P., Davis, R. H., Ferreri, S., Knudtson, M., Koraym, A., et al.
(2009). Health benefits of dietary fiber. Nutr. Rev. 67 (4), 188–205. doi: 10.1111/j.1753-
4887.2009.00189.x
Asoro, F. G., Newell, M. A., Scott, M. P., Beavis, W. D., and Jannink, J. L. (2013).
Genome-wide association study for beta-glucan concentration in elite north American
oat. Crop Sci. 53 (2), 542–553. doi: 10.2135/cropsci2012.01.0039
Beissinger, T. M., Wang, L., Crosby, K., Durvasula, A., Hufford, M. B., and Ross-
Ibarra, J. (2016). Recent demography drives changes in linked selection across the
maize genome. Nat. Plants 2 (7), 1–7. doi: 10.1038/nplants.2016.84
Bekele, W. A., Itaya, A., Boyle, B., Yan, W., Mitchell Fetch, J., and Tinker, N. A.
(2020). A targeted genotyping-by-sequencing tool (rapture) for genomics-assisted
breeding in oat. Theor. Appl. Genet. 133 (2), 653–664. doi: 10.1007/s00122-019-
03496-w
Boland, P., and Lawes, D. A. (1973). The inheritance of the naked grain character in
oats studied in a cross between the naked variety Caesar and the husked variety BO 1/
11. Euphytica 22 (3), 582–591. doi: 10.1007/BF00036659
Bradbury,P.J.,Zhang,Z.,Kroon,D.E.,Casstevens,T.M.,Ramdoss,Y.,and
Buckler, E. S. (2007). TASSEL: software for association mapping of complex traits in
diverse samples. Bioinformatics 23 (19), 2633–2635. doi: 10.1093/bioinformatics/
btm308
Breseghello, F., and Sorrells, M. E. (2006). Association mapping of kernel size and
milling quality in wheat (Triticum aestivum l.) cultivars. Genetics 172 (2), 1165–1177.
doi: 10.1534/genetics.105.044586
Carlson, M. O., Montilla-Bascon, G., Hoekenga, O. A., Tinker, N. A., Poland, J.,
Baseggio, M., et al. (2019). Multivariate genome-wide association analyses reveal the
genetic basis of seed fatty acid composition in oat (Avena sativa l.). G3: Genes Genom
Genet. 9 (9), 2963–2975. doi: 10.1534/g3.119.400228
Cavanagh, C. R., Chao, S., Wang, S., Huang, B. E., Stephen, S., Kiani, S., et al. (2013).
Genome-wide comparative diversity uncovers multiple targets of selection for
improvement in hexaploid wheat landraces and cultivars. Proc. Natl. Acad. Sci. 110
(20), 8057–8062. doi: 10.1073/pnas.1217133110
Chaffin, A. S., Huang, Y. F., Smith, S., Bekele, W. A., Babiker, E., Gnanesh, B. N., et al.
(2016). A consensus map in cultivated hexaploid oat reveals conserved grass synteny
with substantial subgenome rearrangement. Plant Genome 9(2),
plantgenome2015.10.0102. doi: 10.3835/plantgenome2015.10.0102
Cömertpay, G., Habyarimana, E., Baloch, F. S., Güngör, H., Dokuyucu, T., Akkaya,
A., et al. (2018). Geographical description and molecular characterization of genetic
structure and diversity using a 6K SNP array in Turkish oat germplasm. Can. J. Plant
Sci. 99 (1), 12–21. doi: 10.1139/cjps-2017-0293
De Koeyer, D. L., Tinker, N. A., Wight, C. P., Deyl, J., Burrows, V. D., O’donoughue,
L. S., et al. (2004). A molecular linkage map with associated QTLs from a hulless ×
covered spring oat population. Theor. Appl. Genet. 108 (7), 1285–1298. doi: 10.1007/
s00122-003-1556-x
EsveltKlos,K.,Huang,Y.F.,Bekele,W.A.,Obert,D.E.,Babiker,E.,Beattie,A.
D., et al. (2016). Population genomics related to adaptation in elite oat germplasm.
Plant Genome 9 (2), plantgenome2015–10. doi: 10.3835/plantgenome2015.
10.0103
Evanno, G., Regnaut, S., and Goudet, J. (2005). Detecting the number of clusters of
individuals using the software STRUCTURE: a simulation study. Mol. Ecol. 14 (8),
2611–2620. doi: 10.1111/j.1365-294X.2005.02553.x
Eyre-Walker, A., Gaut, R. L., Hilton, H., Feldman, D. L., and Gaut, B. S. (1998).
Investigation of the bottleneck leading to the domestication of maize. Proc. Natl. Acad.
Sci. 95 (8), 4441–4446. doi: 10.1073/pnas.95.8.4441
Fu, Y. B., Peterson, G. W., Scoles, G., Rossnagel, B., Schoen, D. J., and Richards, K.
W. (2003). Allelic diversity changes in 96 Canadian oat cultivars released from 1886 to
2001. Crop Sci. 43 (6), 1989–1995. doi: 10.2135/cropsci2003.1989
Fu, Y. B., Peterson, G. W., Williams, D., Richards, K. W., and Fetch, J. M. (2005).
Patterns of AFLP variation in a core subset of cultivated hexaploid oat germplasm.
Theor. Appl. Genet. 111 (3), 530–539. doi: 10.1007/s00122-005-2044-2
Gui, J., Shen, J., and Li, L. (2011). Functional characterization of evolutionarily
divergent 4-coumarate: coenzyme a ligases in rice. Plant Physiol. 157 (2), 574–586. doi:
10.1104/pp.111.178301
Hartl, D. L., Clark, A. G., and Clark, A. G. (1997). Principles of population genetics
(Vol. 116) (Sunderland: Sinauer associates).
He, X., and Bjørnstad, Å. (2012). Diversity of north European oat analyzed by SSR,
AFLP and DArT markers. Theor. Appl. Genet. 125 (1), 57–70. doi: 10.1007/s00122-012-
1816-8
Hoffman, L. A. (1995). World production and use of oats (Springer, Dordrecht: In
The Oat Crop), 34–61.
Hoffman, D. L. (1999). Inheritance and linkage relationships of morphological and
isozyme loci in as-genome diploid oat (Avena spp.). J. Hered 90 (4), 446–452. doi:
10.1093/jhered/90.4.446
Huang, Y. F., Poland, J. A., Wight, C. P., Jackson, E. W., and Tinker, N. A. (2014).
Using genotyping-by-sequencing (GBS) for genomic discovery in cultivated oat. PloS
One 9 (7), e102448. doi: 10.1371/journal.pone.0102448
Huang, X., Sang, T., Zhao, Q., Feng, Q., Zhao, Y., Li, C., et al. (2010). Genome-wide
association studies of 14 agronomic traits in rice landraces. Nat. Genet. 42 (11), 961–
967. doi: 10.1038/ng.695
Li, Y., Leveau, A., Zhao, Q., Feng, Q., Lu, H., Miao, J., et al. (2021). Subtelomeric
assembly of a multi-gene pathway for antimicrobial defense compounds in cereals. Nat.
Commun. 12 (1), 1–13. doi: 10.1038/s41467-021-22920-8
Lipman, E., Maggioni, L., Knüpffer, H., Ellis, R., Leggett, J. M., Kleijer, G., et al.
(2005). Cereal genetic resources in Europe: Report of a Cereals Network First Meeting, 3-
5 July 2003, Yerevan, Armenia/Report of a Working Group on Wheat, Second Meeting,
22-24 September 2005, La Rochelle, France. Rome, Italy, International Plant Genetic
Resources Institute.
Liu, K., and Muse, S. V. (2005). PowerMarker: an integrated analysis environment
for genetic marker analysis. Bioinformatics 21 (9), 2128–2129. doi: 10.1093/
bioinformatics/bti282
Liu, S., Zhao, L., Liao, Y., Luo, Z., Wang, H., Wang, P., et al. (2020). Dysfunction of
the 4-coumarate: coenzyme a ligase 4CL4 impacts aluminum resistance and lignin
accumulation in rice. Plant J. 104 (5), 1233–1250. doi: 10.1111/tpj.14995
Loskutov, I. G. (2008). On evolutionary pathways of Avena species. Genet. Resour
Crop Evol. 55 (2), 211–220. doi: 10.1007/s10722-007-9229-2
Mangin, B., Siberchicot, A., Nicolas, S., Doligez, A., This, P., and Cierco-Ayrolles, C.
(2012). Novel measures of linkage disequilibrium that correct the bias due to
population structure and relatedness. Heredity 108 (3), 285–291. doi: 10.1038/
hdy.2011.73
Maughan, P. J., Lee, R., Walstead, R., Vickerstaff, R. J., Fogarty, M. C., Brouwer, C. R.,
et al. (2019). Genomic insights from the first chromosome-scale assemblies of oat
(Avena spp.) diploid species. BMC Biol. 17 (1), 1–19. doi: 10.1186/s12915-019-0712-y
Milner, S. G., Jost, M., Taketa, S., Mazon, E. R., Himmelbach, A., Oppermann, M.,
et al. (2019). Genebank genomics highlights the diversity of a global barley collection.
Nat. Genet. 51 (2), 319–326. doi: 10.1038/s41588-018-0266-x
Montilla-Bascon, G., Sanchez-Martı

n, J., Rispail, N., Rubiales, D., Mur, L., Langdon,
T., et al. (2013). Genetic diversity and population structure among oat cultivars and
landraces. Plant Mol. Biol. Rep. 31 (6), 1305–1314. doi: 10.1007/s11105-013-0598-8
Muñoz-Amatriaı

n, M., Cuesta-Marcos, A., Endelman, J. B., Comadran, J., Bonman,
J. M., Bockelman, H. E., et al. (2014). The USDA barley core collection: genetic
diversity, population structure, and potential for genome-wide association studies. PloS
One 9 (4), e94688. doi: 10.1371/journal.pone.0094688
Newell,M.A.,Asoro,F.G.,Scott,M.P.,White,P.J.,Beavis,W.D.,andJannink,J.L.
(2012). Genome-wide association study for oat (Avena sativa l.) beta-glucan concentration
using germplasm of worldwide origin. Theor. Appl. Genet. 125 (8), 1687–1696. doi: 10.1007/
s00122-012-1945-0
Newell,M.A.,Cook,D.,Tinker,N.A.,andJannink,J.L.(2011).Populationstructureand
linkage disequilibrium in oat (Avena sativa l.): implications for genome-wide association
studies. Theor. Appl. Genet. 122 (3), 623–632. doi: 10.1007/s00122-010-1474-7
Newton, A. C., Akar, T., Baresel, J. P., Bebeli, P. J., Bettencourt, E., Bladenopoulos, K.
V., et al. (2011). Cereal landraces for sustainable agriculture. a review. Agron. Sustain
Dev. 30 (2), 237–269. doi: 10.1007/978-94-007-0394-0_10
Othman, R. A., Moghadasian, M. H., and Jones, P. J. (2011). Cholesterol-lowering
effects of oat b-glucan. Nutr. Rev. 69 (6), 299–309. doi: 10.1111/j.1753-
4887.2011.00401.x
Peng, Y., Yan, H., Guo, L., Deng, C., Kang, L., Wang, C., et al. (2022). Reference
genome assemblies reveal the origin and evolution of allohexaploid oat. Nat. Genet. 54
(8), 1248–1258. doi: 10.21203/rs.3.rs-664692/v1
Pritchard, J. K., Stephens, M., and Donnelly, P. (2000). Inference of population
structure using multilocus genotype data. Genetics 155 (2), 945–959. doi: 10.1093/
genetics/155.2.945
Reif, J. C., Zhang, P., Dreisigacker, S., Warburton, M. L., van Ginkel, M., Hoisington,
D., et al. (2005). Wheat genetic diversity trends during domestication and breeding.
Theor. Appl. Genet. 110 (5), 859–864. doi: 10.1007/s00122-004-1881-8
Ren, C. Z., and Yang, C. (2018). Oat cultivars in China (Beijing: China Agriculture
Press).
Rodgers, D. M., Murphy, J. P., and Frey, K. J. (1983). Impact of plant breeding on the
grain yield and genetic diversity of spring oats. Crop Sci. 23 (4), 737–740. doi: 10.2135/
cropsci1983.0011183X002300040032x
Ruwali, Y., Singh, K., Kumar, S., and Kumar, L. (2013). Molecular diversity analysis
in selected fodder and dual purpose oat (Avena sativa l.) genotypes by using random
Wang et al. 10.3389/fpls.2023.1131751
Frontiers in Plant Science frontiersin.org13

amplified polymorphic DNA (RAPD). Afr J. Biotechnol. 12 (22):3425–3429.
doi: 10.5897/AJB2013.12410
Taketa, S., Amano, S., Tsujino, Y., Sato, T., Saisho, D., Kakeda, K., et al. (2008).
Barley grain with adhering hulls is controlled by an ERF family transcription factor
gene regulating a lipid biosynthesis pathway. Proc. Natl. Acad. Sci. 105, 4062–4067. doi:
10.1073/pnas.0711034105
Tamura, K., Stecher, G., Peterson, D., Filipski, A., and Kumar, S. (2013). MEGA6:
molecular evolutionary genetics analysis version 6.0. Mol. Biol. Evol. 30 (12), 2725–
2729. doi: 10.1093/molbev/mst197
Tinker, N. A., Chao, S., Lazo, G. R., Oliver, R. E., Huang, Y. F., Poland, J. A., et al.
(2014). A SNP genotyping array for hexaploid oat. Plant Genome 7(3),
plantgenome2014.03.0010. doi: 10.3835/plantgenome2014.03.0010
Tinker, N. A., Kilian, A., Wight, C. P., Heller-Uszynska, K., Wenzl, P., Rines, H. W.,
et al. (2009). New DArT markers for oat provide enhanced map coverage and global
germplasm characterization. BMC Genomics 10 (1), 1–22. doi: 10.1186/1471-2164-10-39
Tumino,G.,Voorrips,R.E.,Morcia,C.,Ghizzoni,R.,Germeier,C.U.,Paulo,M.J.,etal.
(2017). Genome-wide association analysis for lodging tolerance and plant height in a diverse
European hexaploid oat collection. Euphytica 213 (8), 1–12. doi: 10.1007/s10681-017-1939-8
Tumino, G., Voorrips, R. E., Rizza, F., Badeck, F. W., Morcia, C., Ghizzoni, R., et al.
(2016). Population structure and genome-wide association analysis for frost tolerance
in oat using continuous SNP array signal intensity ratios. Theor. Appl. Genet. 129 (9),
1711–1724. doi: 10.1007/s00122-016-2734-y
Ubert, I. P., Zimmer, C. M., Pellizzaro, K., Federizzi, L. C., and Nava, I. C. (2017).
Genetics and molecular mapping of the naked grains in hexaploid oat. Euphytica 213
(2), 1–16. doi: 10.1007/s10681-017-1836-1
Wabila, C., Neumann, K., Kilian, B., Radchuk, V., and Graner, A. (2019). A tiered
approach to genome-wide association analysis for the adherence of hulls to the
caryopsis of barley seeds reveals footprints of selection. BMC Plant Biol. 19 (1), 1–
12. doi: 10.1186/s12870-019-1694-1
Wang, M., Yan, J., Zhao, J., Song, W., Zhang, X., Xiao, Y., et al. (2012). Genome-wide
association study (GWAS) of resistance to head smut in maize. Plant Sci. 196, 125–131.
doi: 10.1016/j.plantsci.2012.08.004
Winkler, L. R., Michael Bonman, J., Chao, S., Admassu Yimer, B., Bockelman, H.,
and Esvelt Klos, K. (2016). Population structure and genotype–phenotype associations
in a collection of oat landraces and historic cultivars. Front. Plant Sci. 7, 1077. doi:
10.3389/fpls.2016.01077
Yan, H., Bekele, W. A., Wight, C. P., Peng, Y., Langdon, T., Latta, R. G., et al. (2016a).
High-density marker profiling confirms ancestral genomes of avena species and
identifies d-genome chromosomes of hexaploid oat. Theor. Appl. Genet. 129 (11),
2133–2149. doi: 10.1007/s00122-016-2762-7
Yan, H., Martin, S. L., Bekele, W. A., Latta, R. G., Diederichsen, A., Peng, Y., et al.
(2016b). Genome size variation in the genus Avena.Genome 59 (3), 209–220. doi:
10.1139/gen-2015-0132
Yan, H., Zhou, P., Peng, Y., Bekele, W. A., Ren, C., Tinker, N. A., et al. (2020).
Genetic diversity and genome-wide association analysis in Chinese hulless oat
germplasm. Theor. Appl. Genet. 133 (12), 3365–3380. doi: 10.1007/s00122-020-
03674-1
Yang, J., Lee, S. H., Goddard, M. E., and Visscher, P. M. (2011). GCTA: a tool for
genome-wide complex trait analysis. Am. J. Hum. Genet. 88 (1), 76–82. doi: 10.1016/
j.ajhg.2010.11.011
Wang et al. 10.3389/fpls.2023.1131751
Frontiers in Plant Science frontiersin.org14