DOI: 10.5061/dryad.4tmpg4f8v
title: Spatial and temporal genetic variation in Ethiopian barley (Hordeum vulgare L.) landraces as revealed by simple sequence repeat (SSR) markers

description: 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.
description: <b>Plant
 Material </b> 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
 <i>viz</i>: 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
 <i>Hordeum</i> accessions available at Ethiopian Biodiversity
 Institute (EBI) <i>ex-situ </i>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.
 <b>Genotyping
 by SSR markers</b> 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.
 <b>Genetic
 diversity analysis </b> 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, <b>2005</b>), gene diversity (GD, Nei,
 <b>1987</b>), polymorphic information content (PIC, Nagy et
 al., <b>2012</b>), observed heterozygosity (Ho) and the
 expected heterozygosity (He, Berg and Hamrick, <b>1997</b>,
 heterozygosity expected under Hardy–Weinberg equilibrium that accounts for
 both the number and the evenness of alleles), allele richness
 (<i>Ar</i>, El Mousadik and Petit, <b>1996</b>),
 inbreeding coefficient (<i>Fis</i>) and the fixation index 
 (F<sub>ST</sub>) (Weir and Cockerham, <b>1984</b>)
 among populations were calculated using GeneAlEx 6.51b2 software and the
 <i>hierfstat </i>R package (Goudet and Jombart,
 <b>2015</b>). The proportion of the total genetic variance
 contained in a subpopulations (<i>Fst</i>) relative to the
 total genetic variance was computed within each year also using
 <i>hierfstat</i>. <b>Inter-individual genetic distances</b> Nei’s genetic distance (<b>1983</b>) 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. <b>2018</b>).  The inter‐individual genetic distances was calculated using principal components analysis (PCA) using <i>adegenet </i>(Jombart, <b>2008</b>). Principal coordinate analysis (PCoA) was carried out in GeneAlEx version 6.51b2 (Peakall and Smouse, <b>2012</b>) and analysis of molecular variance (AMOVA) was calculated by R package poppr (Kamvar et al. <b>2014</b>). 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, <b>1951</b>), the value of Nm can be estimated from FST, as Nm = (1- F<sub>ST</sub>)/ 4 F<sub>ST</sub>, 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. <b>Spatio-temporal genetic variation</b> 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 <i>adonis </i>of the <i>vegan </i>package (Oksanen et al., <b>2017</b>) 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, <b>2010</b>). 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). <b>Clustering analysis </b> In this study, we searched for genetic groups using discriminant analysis of principal components (DAPC) implemented in the <i>adegenet </i>(Jombart, <b>2008</b>) 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, <b>1992</b>). For each year, we used the function <i>find.clusters </i>to determine the number of clusters and also Bayesian information criterion (<i>BIC</i>) was used to identify the most probable number of clusters (<i>K</i>) 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. <b>Isolation by distance (IBD)</b> 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 <i>mantel.randtest </i>function of the <i>ade4 </i>R package with 9,999 permutations (Dray and Dufour, <b>2007</b>). <b>Spatial structure analysis </b> 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., <b>2007</b>). 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 (<i>spca</i>; Jombart et el., <b>2008</b>). As suggested by Jombart et al. (<b>2008</b>), this spatial multivariate method employs Moran's index (<i>I</i>) of spatial autocorrelation (Moran, <b>1948</b>) to detect global structures. We used the <i>spca </i>function employed in the <i>adegenet </i>(Jombart et al., <b>2008</b>) 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 (<i>n </i>= 9,999) (Jombart et al., <b>2008</b>).
description: No missing values. The only thing required is request letter to
 Ethiopian Biotechnology Institute (EBTi) and Ethiopian Biodiversity
 Institute (EBI).