Two-sample tests for comparing intra-individual genetic sequence diversity between populations

Consider a study of two groups of individuals infected with a population of a genetically related heterogeneous mixture of viruses, and multiple viral sequences are sampled from each person. Based on estimates of genetic distances between pairs of aligned viral sequences within individuals, we develop four new tests to compare intra-individual genetic sequence diversity between the two groups. This problem is complicated by two levels of dependency in the data structure: (i) Within an individual, any pairwise distances that share a common sequence are positively correlated; and (ii) for any two pairings of individuals which share a person, the two differences in intra-individual distances between the paired individuals are positively correlated. The first proposed test is based on the difference in mean intra-individual pairwise distances pooled over all individuals in each group, standardized by a variance estimate that corrects for the correlation structure using U-statistic theory. The second procedure is a nonparametric rank-based analog of the first test, and the third test contrasts the set of subject-specific average intra-individual pairwise distances between the groups. These tests are very easy to use and solve correlation problem (i). The fourth procedure is based on a linear combination of all possible U-statistics calculated on independent, identically distributed sequence subdatasets, over the two levels (i) and (ii) of dependencies in the data, and is more complicated than the other tests but can be more powerful. Although the proposed methods are empirical and do not fully utilize knowledge from population genetics, the tests reflect biology through the evolutionary models used to derive the pairwise sequence distances. The new tests are evaluated theoretically and in a simulation study, and are applied to a dataset of 200 HIV sequences sampled from 21 children.     

Genome scanning tests for comparing amino acid sequences between groups

Summary .   Consider a placebo-controlled preventive HIV vaccine efficacy trial. An HIV amino acid sequence is measured from each volunteer who acquires HIV, and these sequences are aligned together with the reference HIV sequence represented in the vaccine. We develop genome scanning methods to identify positions at which the amino acids in infected vaccine recipient sequences either (A) are more divergent from the reference amino acid than the amino acids in infected placebo recipient sequences or (B) have a different frequency distribution than the placebo sequences, irrespective of a reference amino acid. We consider t -test-type statistics for problem A and Euclidean, Mahalanobis, and Kullback–Leibler-type statistics for problem B. The test statistics incorporate weights to reflect biological information contained in different amino acid positions and mismatches. Position-specific p -values are obtained by approximating the null distribution of the statistics either by a permutation procedure or by a nonparametric estimation. A permutation method is used to estimate a cut-off p -value to control the per comparison error rate at a prespecified level. The methods are examined in simulations and are applied to two HIV examples. The methods for problem B address the general problem of comparing discrete frequency distributions between groups in a high-dimensional data setting.     

Introducing DOTUR, a computer program for defining operational taxonomic units and estimating species richness

Although copious qualitative information describes the members of the diverse microbial communities on Earth, statistical approaches for quantifying and comparing the numbers and compositions of lineages in communities are lacking. We present a method that addresses the challenge of assigning sequences to operational taxonomic units (OTUs) based on the genetic distances between sequences. We developed a computer program, DOTUR, which assigns sequences to OTUs by using either the furthest, average, or nearest neighbor algorithm for each distance level. DOTUR uses the frequency at which each OTU is observed to construct rarefaction and collector's curves for various measures of richness and diversity. We analyzed 16S rRNA gene libraries derived from Scottish and Amazonian soils and the Sargasso Sea with DOTUR, which assigned sequences to OTUs rapidly and reliably based on the genetic distances between sequences and identified previous inconsistencies and errors in assigning sequences to OTUs. An analysis of the two 16S rRNA gene libraries from soil demonstrated that they do not contain enough sequences to support a claim that they contain different numbers of bacterial lineages with statistical confidence (P > 0.05), nor do they contain enough sequences to provide a robust estimate of species richness when an OTU is defined as containing sequences that are no more than 3% different from each other. In contrast, the richness of OTUs at the 3% level in the Sargasso Sea collection began to plateau after the sampling of 690 sequences. We anticipate that an equivalent extent of sampling for soil would require sampling more than 10,000 sequences, almost 100 times the size of typical sequence collections obtained from soil.     

Introducing DOTUR, a Computer Program for Defining Operational Taxonomic Units and Estimating Species Richness

Although copious qualitative information describes the members of the diverse microbial communities on Earth, statistical approaches for quantifying and comparing the numbers and compositions of lineages in communities are lacking. We present a method that addresses the challenge of assigning sequences to operational taxonomic units (OTUs) based on the genetic distances between sequences. We developed a computer program, DOTUR, which assigns sequences to OTUs by using either the furthest, average, or nearest neighbor algorithm for each distance level. DOTUR uses the frequency at which each OTU is observed to construct rarefaction and collector's curves for various measures of richness and diversity. We analyzed 16S rRNA gene libraries derived from Scottish and Amazonian soils and the Sargasso Sea with DOTUR, which assigned sequences to OTUs rapidly and reliably based on the genetic distances between sequences and identified previous inconsistencies and errors in assigning sequences to OTUs. An analysis of the two 16S rRNA gene libraries from soil demonstrated that they do not contain enough sequences to support a claim that they contain different numbers of bacterial lineages with statistical confidence (P > 0.05), nor do they contain enough sequences to provide a robust estimate of species richness when an OTU is defined as containing sequences that are no more than 3% different from each other. In contrast, the richness of OTUs at the 3% level in the Sargasso Sea collection began to plateau after the sampling of 690 sequences. We anticipate that an equivalent extent of sampling for soil would require sampling more than 10,000 sequences, almost 100 times the size of typical sequence collections obtained from soil.     

A Regression Modeling Approach for Describing Patterns of HIV Genetic Variation

We introduce a novel approach for describing patterns of HIV genetic variation using regression modeling techniques. Parameters are defined for describing genetic variation within and between viral populations by generalizing Simpson's index of diversity. Regression models are specified for these variation parameters and the generalized estimating equation framework is used for estimating both the regression parameters and their corresponding variances. Conditions are described under which the usual asymptotic approximations to the distribution of the estimators are met. This approach provides a formal statistical framework for testing hypotheses regarding the changing patterns of HIV genetic variation over time within an infected patient. The application of these methods for testing biologically relevant hypotheses concerning HIV genetic variation is demonstrated in an example using sequence data from a subset of patients from the Multicenter AIDS Cohort Study.     

Distinguishing migration from isolation: a Markov chain Monte Carlo approach

A Markov chain Monte Carlo method for estimating the relative effects of migration and isolation on genetic diversity in a pair of populations from DNA sequence data is developed and tested using simulations. The two populations are assumed to be descended from a panmictic ancestral population at some time in the past and may (or may not) after that be connected by migration. The use of a Markov chain Monte Carlo method allows the joint estimation of multiple demographic parameters in either a Bayesian or a likelihood framework. The parameters estimated include the migration rate for each population, the time since the two populations diverged from a common ancestral population, and the relative size of each of the two current populations and of the common ancestral population. The results show that even a single nonrecombining genetic locus can provide substantial power to test the hypothesis of no ongoing migration and/or to test models of symmetric migration between the two populations. The use of the method is illustrated in an application to mitochondrial DNA sequence data from a fish species: the threespine stickleback (Gasterosteus aculeatus).     

Georgian and kurd mtDNA sequence analysis shows a lack of correlation between languages and female genetic lineages

Mitochondrial DNA sequences from Georgians and Kurds were analyzed in order to test the possible correlation between female lineages and languages in these two neighboring West Eurasian groups. Mitochondrial sequence pools in both populations are very similar despite their different linguistic and prehistoric backgrounds. Both populations present mtDNA lineages that clearly belong to the European gene pool, as shown by 1) similar nucleotide and sequence diversities; 2) a large number of sequences shared with the rest of European samples; 3) nonsignificant genetic distances; and 4) classification of the present lineages into the major European mtDNA haplogroups already described. The outlier position of the populations from the Caucasus according to classical genetic markers is not recognized in the present Georgian mtDNA sequence pool. This result suggests that the differentiation of mtDNA sequences in West Eurasia and the outlier features of Caucasian populations should be attributed to different processes. Moreover, the putative linguistic relationship between Caucasian groups and the Basques, another outlier population within Europe for classical genetic markers, is not detected by the analysis of mtDNA sequences.     

Using HIV transmission networks to investigate community effects in HIV prevention trials

Effective population screening of HIV and prevention of HIV transmission are only part of the global fight against AIDS. Community-level effects, for example those aimed at thwarting future transmission, are potential outcomes of treatment and may be important in stemming the epidemic. However, current clinical trial designs are incapable of detecting a reduction in future transmission due to treatment. We took advantage of the fact that HIV is an evolving pathogen whose transmission network can be reconstructed using genetic sequence information to address this shortcoming. Here, we use an HIV transmission network inferred from recently infected men who have sex with men (MSM) in San Diego, California. We developed and tested a network-based statistic for measuring treatment effects using simulated clinical trials on our inferred transmission network. We explored the statistical power of this network-based statistic against conventional efficacy measures and find that when future transmission is reduced, the potential for increased statistical power can be realized. Furthermore, our simulations demonstrate that the network statistic is able to detect community-level effects (e.g., reduction in onward transmission) of HIV treatment in a clinical trial setting. This study demonstrates the potential utility of a network-based statistical metric when investigating HIV treatment options as a method to reduce onward transmission in a clinical trial setting.     

Interior-branch and bootstrap tests of phylogenetic trees

We have compared statistical properties of the interior-branch and bootstrap tests of phylogenetic trees when the neighbor-joining tree- building method is used. For each interior branch of a predetermined topology, the interior-branch and bootstrap tests provide the confidence values, PC and PB, respectively, that indicate the extent of statistical support of the sequence cluster generated by the branch. In phylogenetic analysis these two values are often interpreted in the same way, and if PC and PB are high (say, > or = 0.95), the sequence cluster is regarded as reliable. We have shown that PC is in fact the complement of the P-value used in the standard statistical test, but PB is not. Actually, the bootstrap test usually underestimates the extent of statistical support of species clusters. The relationship between the confidence values obtained by the two tests varies with both the topology and expected branch lengths of the true (model) tree. The most conspicuous difference between PC and PB is observed when the true tree is starlike, and there is a tendency for the difference to increase as the number of sequences in the tree increases. The reason for this is that the bootstrap test tends to become progressively more conservative as the number of sequences in the tree increases. Unlike the bootstrap, the interior-branch test has the same statistical properties irrespective of the number of sequences used when a predetermined tree is considered. Therefore, the interior-branch test appears to be preferable to the bootstrap test as long as unbiased estimators of evolutionary distances are used. However, when the interior-branch is applied to a tree estimated from a given data set, PC may give an overestimate of statistical confidence. For this case, we developed a method for computing a modified version (P'C) of the PC value and showed that this P'C tends to give a conservative estimate of statistical confidence, though it is not as conservative as PB. In this paper we have introduced a model in which evolutionary distances between sequences follow a multivariate normal distribution. This model allowed us to study the relationships between the two tests analytically.      

Functional Regression Models for Epistasis Analysis of Multiple Quantitative Traits

To date, most genetic analyses of phenotypes have focused on analyzing single traits or analyzing each phenotype independently. However, joint epistasis analysis of multiple complementary traits will increase statistical power and improve our understanding of the complicated genetic structure of the complex diseases. Despite their importance in uncovering the genetic structure of complex traits, the statistical methods for identifying epistasis in multiple phenotypes remains fundamentally unexplored. To fill this gap, we formulate a test for interaction between two genes in multiple quantitative trait analysis as a multiple functional regression (MFRG) in which the genotype functions (genetic variant profiles) are defined as a function of the genomic position of the genetic variants. We use large-scale simulations to calculate Type I error rates for testing interaction between two genes with multiple phenotypes and to compare the power with multivariate pairwise interaction analysis and single trait interaction analysis by a single variate functional regression model. To further evaluate performance, the MFRG for epistasis analysis is applied to five phenotypes of exome sequence data from the NHLBI’s Exome Sequencing Project (ESP) to detect pleiotropic epistasis. A total of 267 pairs of genes that formed a genetic interaction network showed significant evidence of epistasis influencing five traits. The results demonstrate that the joint interaction analysis of multiple phenotypes has a much higher power to detect interaction than the interaction analysis of a single trait and may open a new direction to fully uncovering the genetic structure of multiple phenotypes.     

Sampling strategies for distances between DNA sequences

An international effort is now underway to obtain the DNA sequence for the entire human genome (Watson and Jordan, 1989, Genomics 5, 654-656; Barnhart, 1989, Genomics 5, 657-660). This Human Genome Initiative will generate sequence data from several species other than humans, and will result in several copies per species of at least some regions of the genome. Although the project has generated much interest, it is but one aspect of the widespread effort to generate DNA sequence data. Published sequences are collected in common databases, and release 63 of GenBank in March 1990 contained 40,127,752 bases from 33,337 reported sequences (News from GenBank 3; Mountain View, California: Intelligenetics, Inc., 1990). Large though this database is, it is only about 1% of the number of bases in the human genome. Interpretations of data of such magnitude are going to require the collaborative efforts of biometricians and molecular biologists, and an aim of this paper is to show that there is also a role for readers of this journal in the design of surveys of DNA sequences. Discussion here will center on the use of sequence data in evolutionary studies, where some region of DNA is sequenced in several different species. The object is to infer the evolutionary history of that particular region, or of the species themselves. Statistical issues in the very important studies on sequences to locate and characterize regions responsible for human diseases will not be addressed here. We will discuss appropriate ways of measuring distances between DNA sequences and of predicting the sampling properties of the distances. There are procedures for inferring evolutionary histories for a set of elements that depend on a matrix of distances between each pair of elements, and the precision of resulting trees must be influenced by the precision of the distances. We will show that account needs to be taken of two sampling processes--the sampling of sequences by the investigator ("statistical sampling"), and the sampling of genetic material involved in the formation of offspring from a parental population ("genetic sampling").     

Genetic spatial autocorrelation can readily detect sex-biased dispersal

Sex-biased dispersal is expected to generate differences in the fine-scale genetic structure of males and females. Therefore, spatial analyses of multilocus genotypes may offer a powerful approach for detecting sex-biased dispersal in natural populations. However, the effects of sex-biased dispersal on fine-scale genetic structure have not been explored. We used simulations and multilocus spatial autocorrelation analysis to investigate how sex-biased dispersal influences fine-scale genetic structure. We evaluated three statistical tests for detecting sex-biased dispersal: bootstrap confidence intervals about autocorrelation r values and recently developed heterogeneity tests at the distance class and whole correlogram levels. Even modest sex bias in dispersal resulted in significantly different fine-scale spatial autocorrelation patterns between the sexes. This was particularly evident when dispersal was strongly restricted in the less-dispersing sex (mean distance <200 m), when differences between the sexes were readily detected over short distances. All tests had high power to detect sex-biased dispersal with large sample sizes (n ≥ 250). However, there was variation in type I error rates among the tests, for which we offer specific recommendations. We found congruence between simulation predictions and empirical data from the agile antechinus, a species that exhibits male-biased dispersal, confirming the power of individual-based genetic analysis to provide insights into asymmetries in male and female dispersal. Our key recommendations for using multilocus spatial autocorrelation analyses to test for sex-biased dispersal are: (i) maximize sample size, not locus number; (ii) concentrate sampling within the scale of positive structure; (iii) evaluate several distance class sizes; (iv) use appropriate methods when combining data from multiple populations; (v) compare the appropriate groups of individuals.     

Global phylogeography of a cryptic copepod species complex and reproductive isolation between genetically proximate "populations"

The copepod Eurytemora affinis has a broad geographic range within the Northern Hemisphere, inhabiting coastal regions of North America, Asia, and Europe. A phylogenetic approach was used to determine levels of genetic differentiation among populations of this species, and interpopulation crosses were performed to determine reproductive compatibility. DNA sequences from two mitochondrial genes, large subunit (16S) rRNA (450 bp) and cytochrome oxidase I (COI, 652 bp), were obtained from 38 populations spanning most of the species range and from two congeneric species, E. americana and E. herdmani. Phylogenetic analysis revealed a polytomy of highly divergent clades with maximum sequence divergences of 10% in 16S rRNA and 19% in COI. A power test (difference of a proportion) revealed that amount of sequence data collected was sufficient for resolving speciation events occurring at intervals greater than 300,000 years, but insufficient for determining whether speciation events were approximately simultaneous. Geographic and genetic distances were not correlated (Mantel's test; r = 0.023, P = 0.25), suggesting that populations had not differentiated through gradual isolation by distance. At finer spatial scales, there was almost no sharing of mtDNA haplotypes among proximate populations, indicating little genetic exchange even between nearby sites. Interpopulation crosses demonstrated reproductive incompatibility among genetically distinct populations, including those that were sympatric. Most notably, two geographically distant (4000 km) but genetically proximate (0.96% 16S, 0.15% COI) populations exhibited asymmetric reproductive isolation at the F2 generation. Large genetic divergences and reproductive isolation indicate that the morphologically conservative E. affinis constitutes a sibling species complex. Reproductive isolation between genetically proximate populations underscores the importance of using multiple measures to examine patterns of speciation.     

Impact of CCR5delta32 host genetic background and disease progression on HIV-1 intrahost evolutionary processes: efficient hypothesis testing through hierarchical phylogenetic models

The interplay between C-C chemokine receptor type 5 (CCR5) host genetic background, disease progression, and intrahost HIV-1 evolutionary dynamics remains unclear because differences in viral evolution between hosts limit the ability to draw conclusions across hosts stratified into clinically relevant populations. Similar inference problems are proliferating across many measurably evolving pathogens for which intrahost sequence samples are readily available. To this end, we propose novel hierarchical phylogenetic models (HPMs) that incorporate fixed effects to test for differences in dynamics across host populations in a formal statistical framework employing stochastic search variable selection and model averaging. To clarify the role of CCR5 host genetic background and disease progression on viral evolutionary patterns, we obtain gp120 envelope sequences from clonal HIV-1 variants isolated at multiple time points in the course of infection from populations of HIV-1-infected individuals who only harbored CCR5-using HIV-1 variants at all time points. Presence or absence of a CCR5 wt/Δ32 genotype and progressive or long-term nonprogressive course of infection stratify the clinical populations in a two-way design. As compared with the standard approach of analyzing sequences from each patient independently, the HPM provides more efficient estimation of evolutionary parameters such as nucleotide substitution rates and d(N)/d(S) rate ratios, as shown by significant shrinkage of the estimator variance. The fixed effects also correct for nonindependence of data between populations and results in even further shrinkage of individual patient estimates. Model selection suggests an association between nucleotide substitution rate and disease progression, but a role for CCR5 genotype remains elusive. Given the absence of clear d(N)/d(S) differences between patient groups, delayed onset of AIDS symptoms appears to be solely associated with lower viral replication rates rather than with differences in selection on amino acid fixation.     

High-precision, whole-genome sequencing of laboratory strains facilitates genetic studies

Whole-genome sequencing is a powerful technique for obtaining the reference sequence information of multiple organisms. Its use can be dramatically expanded to rapidly identify genomic variations, which can be linked with phenotypes to obtain biological insights. We explored these potential applications using the emerging next-generation sequencing platform Solexa Genome Analyzer, and the well-characterized model bacterium Bacillus subtilis. Combining sequencing with experimental verification, we first improved the accuracy of the published sequence of the B. subtilis reference strain 168, then obtained sequences of multiple related laboratory strains and different isolates of each strain. This provides a framework for comparing the divergence between different laboratory strains and between their individual isolates. We also demonstrated the power of Solexa sequencing by using its results to predict a defect in the citrate signal transduction pathway of a common laboratory strain, which we verified experimentally. Finally, we examined the molecular nature of spontaneously generated mutations that suppress the growth defect caused by deletion of the stringent response mediator relA. Using whole-genome sequencing, we rapidly mapped these suppressor mutations to two small homologs of relA. Interestingly, stable suppressor strains had mutations in both genes, with each mutation alone partially relieving the relA growth defect. This supports an intriguing three-locus interaction module that is not easily identifiable through traditional suppressor mapping. We conclude that whole-genome sequencing can drastically accelerate the identification of suppressor mutations and complex genetic interactions, and it can be applied as a standard tool to investigate the genetic traits of model organisms.     

Genetic structures and linguistic boundaries in Italy: a microregional approach.

The amount of genetic data (sequences, gene frequencies, and isonymy) available for the Province of Ferrara, Italy, makes this area one of the world's best known. In an effort to infer the underlying demographic processes, we studied the province's population structure by comparing geological, palaeoclimatic, archeological, historical, and linguistic data. This multilevel approach allowed us to date some characteristics of the population structure from prehistoric times to the Roman and Middle Ages, and to detect overlapping biological, cultural, and geographic boundaries. To detect linguistic boundaries within this area we turned pronunciation differences into phonetic notation. We then computed pairwise distances by using methods for multiple genetic sequence analysis, in order to obtain a distance matrix of the overall pronunciation variability. This approach enabled us to test the association among linguistic, geographical, and genetic distance matrices using the same statistical tests. Results indicate that demographic phenomena can be traced in an area as small as the Province of Ferrara and that, on a microregional scale, recent events may have influenced important aspects of the overall genetic variation.     

Landscape connectivity influences gene flow in a roe deer population inhabiting a fragmented landscape: an individual-based approach

Changes in agricultural practices and forest fragmentation can have a dramatic effect on landscape connectivity and the dispersal of animals, potentially reducing gene flow within populations. In this study, we assessed the influence of woodland connectivity on gene flow in a traditionally forest-dwelling species--the European roe deer--in a fragmented landscape. From a sample of 648 roe deer spatially referenced within a study area of 55 x 40 km, interindividual genetic distances were calculated from genotypes at 12 polymorphic microsatellite loci. We calculated two geographical distances between each pair of individuals: the Euclidean distance (straight line) and the 'least cost distance' (the trajectory that maximizes the use of wooded corridors). We tested the correlation between genetic pairwise distances and the two types of geographical pairwise distance using Mantel tests. The correlation was better using the least cost distance, which takes into account the distribution of wooded patches, especially for females (the correlation was stronger but not significant for males). These results suggest that in a fragmented woodland area roe deer dispersal is strongly linked to wooded structures and hence that gene flow within the roe deer population is influenced by the connectivity of the landscape.     

THE COMPARISON OF QUANTITATIVE GENETIC PARAMETERS BETWEEN POPULATIONS

A statistical method for comparing matrices of genetic variation and covariation between groups (e.g., species, populations, a single population grown in distinct environments) is proposed. This maximum-likelihood method provides a test of the overall null hypothesis that two covariance component matrices are identical. Moreover, when the overall null hypothesis is rejected, the method provides a framework for isolating the particular components that differ significantly between the groups. Simulation studies reveal that discouragingly large experiments are necessary to obtain acceptable power for comparing genetic covariance component matrices. For example, even in cases of a single trait measured on 900 individuals in a nested design of 100 sires and three dams per sire in each population, the power was only about 0.5 when additive genetic variance differed by a factor of 2.5. Nevertheless, this flexible method makes valid comparison of covariance component matrices possible.     

Genetic Clustering of Hepatitis C Virus Strains and Severity of Recurrent Hepatitis after Liver Transplantation

The influence of viral factors on the severity of hepatitis C virus (HCV)-related liver disease is controversial. We studied 68 liver transplant patients with recurrent hepatitis C, of whom 53 were infected by genotype 1 strains. Relationships between core sequences, serum HCV RNA levels, and fibrosis scores for each patient were analyzed in pairwise fashion 5 years after transplantation. We used Mantel's test, a matrix correlation method, to evaluate the correspondence between measured genetic distances and observed phenotypic differences. No clear relationship was found when all 68 patients were analyzed. In contrast, when the 53 patients infected by genotype 1 strains were analyzed, a strong positive relationship was found between genetic distance and differences in 5-year fibrosis scores (P = 0.001) and differences in virus load (P = 0.009). In other words, the smaller the genetic distance between two patients' viral core sequences, the smaller the difference between the two patients' fibrosis scores and viral replication levels. No relationship was found between genetic distance and differences in age, sex, or immunosuppression. In multivariate analysis, the degree of fibrosis was negatively related to the virus load (r = -0.68; P = 0.003). In the particular setting of liver transplantation, and among strains with closely related phylogenetic backgrounds (genotype 1), this study points to a correlation between the HCV genetic sequence and the variability of disease expression.     

Analysis of genetic diversity of Guizotia abyssinica from Ethiopia using inter simple sequence repeat markers

Within and among population genetic diversity of 37 Guizotia abyssinica populations from Ethiopia were analyzed using inter simple sequence repeats (ISSRs). Five primers amplified a total of 118 genomic DNA fragments across a total of 370 individuals of which 106 were polymorphic (89.83%). The average number of polymorphic bands per primer was 21.2. More bands were generated by primer UBC 888 (BDB(CA)(7.) The total genetic diversity (Ht) and the coefficient of genetic differentiation (Gst) were 0.4115 and 0.0918 respectively, while the within population genetic diversity (Hs) and the among population genetic diversity(Dst) were 0.3738 and 0.03776 respectively suggesting more variability within the populations than among them. The standard genetic distances between the G. abyssinica populations of the eight regions ranged from 0.0281 (between Wollo and Gojam) to 0.1148 (between Jimma and Hararghe). Generally, the standard genetic distances are smaller between populations of neighboring regions and highest between those of Jimma and the other regions, ranging from 0.0696 (between Jimma and Shewa) to 0.1148 (between Jimma and Hararghe). The ISSR based UPGMA clustering using the standardized genetic distances matrix also placed populations from neighboring regions closer than those from farther apart areas, while the UPGMA clustering by regions based on the standard genetic distances produced three clusters following the proximity and the contiguity of the regions. The mean Shannon Weaver diversity indices for the populations of the eight regions ranged from 0.8197 (Jimma) to 0.9176 (Hararghe), with a mean of 0.8841 for the whole material.