10.5061/DRYAD.4TMPG4F8V
Dido, Allo Aman
0000-0001-9208-961X
Ethiopian Biotechnology Institute
Singh, B.J.K
K.L.E.F. Department of Bio-Technology
Assefa, Ermias
0000-0002-1207-2971
Ethiopian Biotechnology Institute
Krishna, M.S.R.
K.L.E.F. Department of Bio-Technology
Degefu, Dawit
Ethiopian Institute of Agricultural Research
Tesfaye, Kassahun
0000-0002-4046-4657
Ethiopian Biotechnology Institute
Spatial and temporal genetic variation in Ethiopian barley (Hordeum
vulgare L.) landraces as revealed by simple sequence repeat (SSR) markers
Dryad
dataset
2021
FOS: Agricultural biotechnology
en
248851 bytes
2
CC0 1.0 Universal (CC0 1.0) Public Domain Dedication
Ethiopia is a center of diversity for barley (Hordeum vulgare L.) and it
is grown across different agro-ecologies of the country. Unraveling
population structure and gene flow status on temporal scales assists an
evaluation of the consequences of physical, demographic as well as overall
environmental changes on the stability and persistence of populations.
Here, we examine spatial and temporal genetic variation within and among
barley landrace samples collected over a period of four decades
(1976-2017), using simple sequence repeat (SSR) markers. Our objective was
to evaluate spatial and temporal changes in barley population connectivity
associated with the closure of geographic origin and time periods. Low to
strong genetic diversity was observed among the landraces and STRUCTURE,
Neighbour joining tree and Discriminant Analysis of Principal Component
analysis revealed three clusters. The cluster analysis revealed a close
relationship between landraces along geographic proximity with genetic
distance increases along with geographic distance. The grouping of
landraces based on altitudinal classes was influenced by geographic
proximity. From AMOVA year categories, it was observed that within
population genetic diversity much higher than between population genetic
diversity and that the temporal differentiation is considerably smaller.
The low to strong genetic differentiation between landraces from various
geographic origins could be attributed to gene flow across the region as a
consequence of seed exchange among farmers. Nevertheless, we found some
connectivity between changes in population dynamics as well as
contemporary gene flow. The results demonstrate that this set of SSRs was
highly informative and was useful in generating a meaningful
classification of barley germplasms. Furthermore, our data also suggest
that landraces are a source of valuable germplasm for sustainable
agriculture in the context of future climate change, and that in-situ
conservation strategies based on farmers use can conserve the genetic
identity of landraces while allowing adaptation to local-environments.
Plant Material A total of 384 barley genotypes, including 376 landraces
and 8 cultivars were used in this study. The commercial cultivars used in
this analysis include Abdanie, Guta, Dafo, HB-1964, HB-1966, HB-42,
Ardu-12-60B and Aruso (six-rowed barley). These improved commercial
varieties were obtained from Holetta and Sinana Agricultural Research
Centers in the central and southeastern highlands of Ethiopia,
respectively. On the other hand, the landraces were obtained from the
Ethiopian Biodiversity Institute (EBI) along with their passport data. For
data analysis, the improved varieties were only used to study the
relationship within and among barley genotypes. Landraces from regions
with sample size less than five were also included in adjacent regions to
reduce experimental error due to small sample size. This reduced the 42
agro-ecological zones from which the landraces were originally drawn to 15
zones viz: Oromia (six zones), Amhara (three zones), Tigray (two zones),
Southern Nations, Nationalities and Peoples, SNNP (three zones) and
Benishangul Gumuz (one zone). The 384 collected barley landraces comprised
88 landraces collected from Amhara, 188 from Oromia, 42 from SNNP, 57 from
Tigray and 9 from Benishangul Gumuz. Major barley growing highland regions
of Ethiopia, the Oromia and Amhara regions, have been represented by more
samples. The representative samples were carefully selected among Hordeum
accessions available at Ethiopian Biodiversity Institute (EBI) ex-situ
Genebank and those representing different geographical locations of the
country together with their passport data. All landraces are of spring
growth type of which 178 are two-rowed, 186 are six-rowed and 20 are
irregular barley type. Genotyping by SSR markers Genomic DNA was extracted
by the CTAB method (Doyle 1991) from fresh leaves of sampled individuals.
A total of 10 single individual per accession were samples and bulked for
genomic DNA extraction. A total of 49 SSR markers were selected for
analysis, covering the seven chromosomes of barley genome. Genetic
diversity analysis For each region (locality) and each year, summary
statistics, such as allele number per locus (Na), number of effective
allele (Ne), Shannon’s information index (I, Keylock, 2005), gene
diversity (GD, Nei, 1987), polymorphic information content (PIC, Nagy et
al., 2012), observed heterozygosity (Ho) and the expected heterozygosity
(He, Berg and Hamrick, 1997, heterozygosity expected under Hardy–Weinberg
equilibrium that accounts for both the number and the evenness of
alleles), allele richness (Ar, El Mousadik and Petit, 1996), inbreeding
coefficient (Fis) and the fixation index (FST) (Weir and Cockerham, 1984)
among populations were calculated using GeneAlEx 6.51b2 software and the
hierfstat R package (Goudet and Jombart, 2015). The proportion of the
total genetic variance contained in a subpopulations (Fst) relative to the
total genetic variance was computed within each year also using hierfstat.
Inter-individual genetic distances Nei’s genetic distance (1983) was
calculated and used for unrooted phylogeny reconstruction based on UPGMA
methods as implemented by PowerMarker software and the tree was visualized
using MEGA-X version 10.2.2 (Sudhir et al. 2018). The inter‐individual
genetic distances was calculated using principal components analysis (PCA)
using adegenet (Jombart, 2008). Principal coordinate analysis (PCoA) was
carried out in GeneAlEx version 6.51b2 (Peakall and Smouse, 2012) and
analysis of molecular variance (AMOVA) was calculated by R package poppr
(Kamvar et al. 2014). Linear regression analysis of the PIC, Shannon
Wiener index and PI with altitude and longitude was conducted using Excel.
By inverting Wright's formula (Wright, 1951), the value of Nm can be
estimated from FST, as Nm = (1- FST)/ 4 FST, where `N` is the size of each
population and `m` is the migration rate between populations. This
approach is effective to estimate gene flow indirectly. Spatio-temporal
genetic variation To evaluate the effects of sampling sites and year of
sampling on patterns of genetic variation, we performed a
permutation-based multivariate analysis of variance by using the function
adonis of the vegan package (Oksanen et al., 2017) in R. This method
partitions sum of squares for distance matrices in a manner similar to
AMOVA, but allows for both nested and crossed factors (Paradis, 2010). We
evaluated the effects of sampling sites and year of sampling as cross
check factors on the matrix of individual genetic distances. Statistical
significance was assessed using 9,999 permutations. Given the signal of
temporal variability observed, subsequent analyses were done for each year
separately. Due to variation in the number of sampling sites and the
number of individuals sampled per site among years, we performed a
rarefied bootstrap to normalize for the minimum number of sites per year
and the minimum number of individuals per site to ensure that there was no
bias due to the unbalanced sampling. We subsampled the data keeping only
12 sites per year and 5 individuals per site and performed the analysis of
molecular variance (AMOVA). Clustering analysis In this study, we searched
for genetic groups using discriminant analysis of principal components
(DAPC) implemented in the adegenet (Jombart, 2008) package in R. DAPC
maximizes differences among clusters while minimizing variation within but
does not rely on a particular population genetic model, such as
Hardy–Weinberg equilibrium, which is unrealistic for out breeding
populations (Whitlock, 1992). For each year, we used the function
find.clusters to determine the number of clusters and also Bayesian
information criterion (BIC) was used to identify the most probable number
of clusters (K) present in the data. Discriminant analysis of principal
component (DAPC) provides membership probabilities to these clusters for
each individual, which we examined for geographic structure. Isolation by
distance (IBD) We evaluated for IBD by testing the correlation between
genetic distance and the geographic Euclidean distance between all pairs
of individuals. Significance of the correlation between the two distance
matrices was assessed by a Mantel test using the mantel.randtest function
of the ade4 R package with 9,999 permutations (Dray and Dufour, 2007).
Spatial structure analysis The combination of genetic and geographic
information can improve our ability to identify loosely differentiated
populations and can give us precise spatial locations of genetic barriers
or hidden clusters (Storfer et al., 2007). Given the weak overall
structure (i.e., clusters and IBD; see above), we also tested for cryptic
spatial genetic structure within each year using spatial principal
component analysis (spca; Jombart et el., 2008). As suggested by Jombart
et al. (2008), this spatial multivariate method employs Moran's index
(I) of spatial autocorrelation (Moran, 1948) to detect global structures.
We used the spca function employed in the adegenet (Jombart et al., 2008)
of R package. We used the inverse distance analysis method for testing
linkages in the system, given that: (a) Sampling sites were unevenly
spread over the study area; (b) we had no a priori hypothesis about their
connectivity. Significance was checked using permutation test (n = 9,999)
(Jombart et al., 2008).
No missing values. The only thing required is request letter to Ethiopian
Biotechnology Institute (EBTi) and Ethiopian Biodiversity Institute (EBI).