Estimates Of Genetic Diversity Research Articles

Evolutionary history of the roan antelope across its African range

AbstractAimPhylogeographic studies on savanna ungulates have extensively evaluated genetic patterns mostly related to Pleistocene climatic oscillations. We address this subject through a comprehensive assessment across the pan‐African range of the roan antelope, assessing whether climatic oscillations or natural physical barriers play a predominant role in the evolutionary history of the species. We also address the spatial concordance of the genetic structure with the currently recognized subspecies.LocationSub‐Saharan Africa.TaxonRoan antelope (Hippotragus equinus).MethodsWe genotyped 43 microsatellite loci and sequenced whole‐mitochondrial genomes for 131 individuals across the species' entire African range. We performed spatial analyses of genetic diversity for contemporary and historical samples and calculated overall patterns of genetic differentiation and structure for both marker types. We also estimated the timing of divergence events and demographic trends, correlating these with the species’ biotic attributes as well as abiotic features shaping African savanna habitats.ResultsOur analyses uncovered highly structured clusters and contact zones across the distribution of the roan antelope, including five nuclear groups and four mitochondrial lineages. The north‐west group had the highest level of intra‐group diversity as well as inter‐group divergence and represents the oldest vicariant event. The central and southern groups had the lowest intra‐group diversity with low divergence values separating them, suggesting a more recent ancestry for these groups. All population groups showed signals of demographic stability over time showed signals of demographic stability over a demographic decline during the Holocene.Main conclusionsThe roan antelope exhibits significant population structure across its African range. This structure is largely associated with natural physical barriers, whereas contact zones could more easily be explained by climatic events. Based on estimates of genetic diversity, we propose a West African ancestry for this species with subsequent eastward and southern range expansions, as well as the persistence of stable population numbers within refugia. A general trend of population size decreases likely reflects Holocene extreme climatic events and increased human pressure.

Estimation of genetic variance contributed by a quantitative trait locus: correcting the bias associated with significance tests.

The Beavis effect in quantitative trait locus (QTL) mapping describes a phenomenon that the estimated effect size of a statistically significant QTL (measured by the QTL variance) is greater than the true effect size of the QTL if the sample size is not sufficiently large. This is a typical example of the Winners' curse applied to molecular quantitative genetics. Theoretical evaluation and correction for the Winners' curse have been studied for interval mapping. However, similar technologies have not been available for current models of QTL mapping and genome-wide association studies where a polygene is often included in the linear mixed models to control the genetic background effect. In this study, we developed the theory of the Beavis effect in a linear mixed model using a truncated noncentral Chi-square distribution. We equated the observed Wald test statistic of a significant QTL to the expectation of a truncated noncentral Chi-square distribution to obtain a bias-corrected estimate of the QTL variance. The results are validated from replicated Monte Carlo simulation experiments. We applied the new method to the grain width (GW) trait of a rice population consisting of 524 homozygous varieties with over 300 k single nucleotide polymorphism markers. Two loci were identified and the estimated QTL heritability were corrected for the Beavis effect. Bias correction for the larger QTL on chromosome 5 (GW5) with an estimated heritability of 12% did not change the QTL heritability due to the extremely large test score and estimated QTL effect. The smaller QTL on chromosome 9 (GW9) had an estimated QTL heritability of 9% reduced to 6% after the bias-correction.

Estimation of additive and non-additive genetic variances of average daily gain traits in Adani goats

• Dominance and epistasis effects are important for the average daily gain traits and could be added to evaluation models. • Adding the non-additive genetic effects to models led to increase the accuracy of estimated breeding values. • The adgbww3 and adgbww6 traits can be the basis of selection for the next generation. Considering the dominance and epistasis effects in the analysis can increase the accuracy of estimating breeding values. The objective of this study was to fit the best model for each average daily gain trait (average daily gain from birth to weaning (adgbwww), from birth to 3 months (adgbww3), from birth to 6 months (adgbww6), from weaning to 3 months (adgwww3), from 3 to 6 months (adgw3w6), from 6 to 9 months (adgw6w9), and from 9 to 12 months (adgw9w12)) and the estimation of the genetic parameters and variance components, especially non-additive genetic effects, in Adani goats. Analyses were carried out using the Bayesian method via the Gibbs sampler animal model by fitting 18 different models. With the best model, direct heritability estimates were 0.093, 0.250, 0.256, 0.084, 0.036, 0.048, and 0.151 for adgbwww, adgbww3, adgbww6, adgwww3, adgw3w6, adgw6w9 and adgw9w12 traits, respectively. Maternal genetic and maternal permanent environmental effects were significant only for adgbwww trait. Dominance and epistasis effects were significant almost for all traits and as a proportion of phenotypic variance was the range from 0.068 to 0.221 and 0.106 to 0.237, respectively. Adding dominance and epistasis effects to models reduced the error variance and the accuracy of estimating breeding values was increased. The accuracy of breeding values of these traits with the best models ranged from 0.456 to 0.674, 0.493 to 0.656, and 0.424 to 0.674 for all animals, 10 % of best males and 50 % of the best females, respectively. The result of the present study suggests that dominance and epistasis effect was important for average daily gain traits of Adani goats and should be included in evaluation models.

A beginner's guide to low-coverage whole genome sequencing for population genomics.

Low-coverage whole genome sequencing (lcWGS) has emerged as a powerful and cost-effective approach for population genomic studies in both model and nonmodel species. However, with read depths too low to confidently call individual genotypes, lcWGS requires specialized analysis tools that explicitly account for genotype uncertainty. A growing number of such tools have become available, but it can be difficult to get an overview of what types of analyses can be performed reliably with lcWGS data, and how the distribution of sequencing effort between the number of samples analysed and per-sample sequencing depths affects inference accuracy. In this introductory guide to lcWGS, we first illustrate how the per-sample cost for lcWGS is now comparable to RAD-seq and Pool-seq in many systems. We then provide an overview of software packages that explicitly account for genotype uncertainty in different types of population genomic inference. Next, we use both simulated and empirical data to assess the accuracy of allele frequency, genetic diversity, and linkage disequilibrium estimation, detection of population structure, and selection scans under different sequencing strategies. Our results show that spreading a given amount of sequencing effort across more samples with lower depth per sample consistently improves the accuracy of most types of inference, with a few notable exceptions. Finally, we assess the potential for using imputation to bolster inference from lcWGS data in nonmodel species, and discuss current limitations and future perspectives for lcWGS-based population genomics research. With this overview, we hope to make lcWGS more approachable and stimulate its broader adoption.

Estimation of genetic variability and frequency distribution in F2 generation of rice under normal and deficit water supply

The investigation was conducted at the experimental farm of the Rice Research and Training Centre, Kafr el Sheikh, Egypt, during the summer seasons from 2017 to 2019 using the experimental material consisting two populations with their two parents (P1, P2, F1 and F2) to study the variability in the F2 population of four crosses: IR 78,936-B-B-B-B (water deficit tolerant) x Giza 177 (water deficit sensitive), FL-496 (moderately water deficit tolerant) x Giza 177, IR 78,936-B-B-B-B (water deficit tolerant) x Giza 178 (moderately water deficit tolerant), FL-496 × Giza 178 under normal (NWS) and deficit water supply (DWS). The experiment was conducted in a randomized complete block design with three replications. The results indicated that the phenotypic values of the measured characters were significantly different between the two parents of all the studied crosses under the two water supply conditions. The means of all the studied characters under DWS were lower than the means under NWS, but the yields and the yield components under DWS varied greatly among the parents due to stress. The averages of the measured traits of the F1 plants and the F2 populations in all crosses were near the averages of those of the parents. The results showed no consistent reduction in heritability under DWS compared to NWS. High heritability in a broad sense coupled with high genetic advance (GA) was observed for grain yield in Cross 1 under the two studied conditions offering good scope for selection.

Genetic Diversity and Population Structure of Capirona (Calycophyllum spruceanum Benth.) from the Peruvian Amazon Revealed by RAPD Markers

Capirona (Calycophyllum spruceanum Benth.) is a tree species of commercial importance widely distributed in South American forests that is traditionally used for its medicinal properties and wood quality. Studies on this tree species have been focused mainly on wood properties, propagation, and growth. However, genetic studies on capirona have been very limited to date. Currently, it is possible to explore genetic diversity and population structure in a fast and reliable manner by using molecular markers. We here used 10 random amplified polymorphic DNA (RAPD) markers to analyze the genetic diversity and population structure of 59 samples of capirona that were sampled from four provinces located in the eastern region of the Peruvian amazon. A total of 186 bands were manually scored, generating a 59 × 186 presence/absence matrix. A dendrogram was generated using the UPGMA clustering algorithm, and, similar to the principal coordinate analysis (PCoA), it showed four groups that correspond to the geographic origin of the capirona samples (LBS, Irazola, Masisea, Iñapari). Similarly, a discriminant analysis of principal components (DAPC) and STRUCTURE analysis confirmed that capirona is grouped into four clusters. However, we also noticed that a few samples were intermingled. Genetic diversity estimation was conducted considering the four groups (populations) identified by STRUCTURE software. AMOVA revealed the greatest variation within populations (71.56%) and indicated that variability among populations is 28.44%. Population divergence (Fst) between clusters 1 and 4 revealed the highest genetic difference (0.269), and the lowest Fst was observed between clusters 3 and 4 (0.123). RAPD markers were successful and effective. However, more studies are needed, employing other molecular tools. To the best of our knowledge, this is the first investigation employing molecular markers in capirona in Peru considering its natural distribution, and as such it is hoped that this helps to pave the way towards its genetic improvement and the urgent sustainable management of forests in Peru.

Genetic parameters and genetic trends of conformation and management traits in Dairy Gir cattle

ABSTRACT The objective of this study was to estimate genetic parameters and genetic trends of different conformation and management traits regularly measured within the context of the National Dairy Gir Breeding Program (PNMGL). The estimation of genetic and residual variances for each trait was performed using average information restricted maximum likelihood (AI-REML) procedure in AIREMLF90 program software. The population was divided into three subpopulations constituted by measured females (with phenotype records), all females, and males. Linear regressions were applied for each trait, considering two periods of birth (1st period: 1938-1996; 2nd period: 1997-2012). The estimated heritability of conformation and management traits varied from 0.01 to 0.53, denoting a perspective of genetic improvement through selection and corrective matings for purebred Dairy Gir populations. The average genetic changes in conformation and management traits were, in general, variable and inexpressive, showing that the selection of Dairy Gir may have had been directed essentially to increase milk yield. The analysis of the two periods of birth indicated that some linear traits present progress (although inexpressive) in the 2nd period (more recent period).

Genetic diversity and structure in Arizona pronghorn following conservation efforts

AbstractArizona pronghorn (Antilocapra americana) population numbers have declined over the last century due to unregulated‐harvest, population fragmentation, urban expansion, and habitat loss. Captive breeding, reintroductions, and translocations have helped to curb decline and boost population numbers of the endangered Sonoran subspecies (A. a. sonoriensis). To assess the effect of on‐going management actions on the Sonoran subspecies, we collected multi‐locus genotype data and performed tests of genetic differentiation and population structure in comparison to the non‐endangered American subspecies (A. a. americana). We provide updated estimates of genetic diversity and relatedness to serve as a benchmark for future management toward further recovery of Sonoran pronghorn. Management actions have upheld distinction between the two subspecies in Arizona and stemmed further genetic diversity loss while avoiding an increase in inbreeding within the captive‐bred Sonoran population.

Individual haplotyping of whale sharks from seawater environmental DNA.

Population genetic data can provide valuable information on the demography of a species. For rare and elusive marine megafauna, samples for generating the data are traditionally obtained from tissue biopsies, which can be logistically difficult and expensive to collect and require invasive sampling techniques. Analysis of environmental DNA (eDNA) offers an alternative, minimally invasive approach to provide important genetic information. Although eDNA approaches have been studied extensively for species detection and biodiversity monitoring in metabarcoding studies, the potential for the technique to address population-level questions remains largely unexplored. Here, we applied "eDNA haplotyping" to obtain estimates of the intraspecific genetic diversity of a whale shark (Rhincodon typus) aggregation at Ningaloo reef, Australia. Over 2weeks, we collected seawater samples directly behind individual sharks prior to taking a tissue biopsy sample from the same animal. Our data showed a 100% match between mtDNA sequences recovered in the eDNA and tissue sample for all 28 individuals sampled. In the seawater samples, >97% of all reads were assigned to six dominant haplotypes, and a clear dominant signal (~99% of sample reads) was recovered in each sample. Our study demonstrates accurate individual-level haplotyping from seawater eDNA. When DNA from one individual clearly dominates each eDNA sample, it provides many of the same opportunities for population genetic analyses as a tissue sample, potentially removing the need for tissue sampling. Our results show that eDNA approaches for population-level analyses have the potential to supply critical demographic data for the conservation and management of marine megafauna.

​​Genetic Divergence in Leafy Mustard (Brassica juncea var. rugosa) Germplasm Grown under Tarai Condition of Uttarakhand

Background: Mustard represents a rich diversity and widely cultivated in 23 states and union territories of India. However, much of this diversity is concentrated in the Indo-Gangetic plains and the sub-mountain Himalayas. Genetic diversity plays a significant role in plant improvement because a hybrid between the lines of diverse origin usually display a greater heterosis than those between closely related ones which permit the selection of genetically divergent plants to obtain the desirable recombination of segregating generation. Therefore, the present study was undertaken to assess “Genetic Divergence in Leafy Mustard (Brassica juncea. var. rugosa) germplasm grown under Tarai condition of Uttarakhand” and to identify divergent parents for hybridization program, which would provide superior transgressive segregants from collected germplasm. Methods: The present investigation consisted of thirty-two genotypes of leafy mustard and the research was carried out at Vegetable Research Centre (VRC), G.B. Pant University of Agriculture and Technology, Pantnagar, U.S. Nagar (Uttarakhand) in rabi season of 2015-2016. Mustard genotypes were sown in randomized block design with three replications in field and data were observed for seventeen quantitative and qualitative characters. The estimation of genetic divergence was done with the help of Mahalonobis D2 statistic as suggested by Rao (1952). Cluster analysis by Tocher method for all the traits was done. Result: Thirty two germplasm of leafy mustard for different characters and grouped them into six clusters using Mahalanobis D2 statistic. The analysis revealed the maximum inter cluster distance was (20534.12) between cluster V and cluster VI so, we can create variation by inter mating genotypes from these two clusters to each other and the maximum intra cluster distance in cluster III (441.91) with six germplasm. It means we can intermate genotypes of this cluster with each other (2014/MGVAR-2, FS-13-1, FS-13-4, 2014/MGVAR-4, PRHC-12-9-1, PRHC-12-7-2, FS-13-3 and Pusa Sag 1) to create variation in next generations. The clustering pattern could be utilized in selection of parents for crossing and deciding the best cross combinations which may generate the highest possible variability for various traits.

Joint Modeling of Genetics and Field Variation in Plant Breeding Trials Using Relationship and Different Spatial Methods: A Simulation Study of Accuracy and Bias

Modelling field spatial patterns is standard practice for the analysis of plant breeding. Jointly fitting the genetic relationship among individuals and spatial information enables better separability between the variance due to genetics and field variation. This study aims to quantify the accuracy and bias of estimative parameters using different approaches. We contrasted three settings for the genetic term: no relationship (I), pedigree relationship (A), and genomic relationship (G); and a set of approaches for the spatial variation: no-spatial (NS), moving average covariate (MA), row-column adjustment (RC), autoregressive AR1 × AR1 (AR), spatial stochastic partial differential equations, or SPDE (SD), nearest neighbor graph (NG), and Gaussian kernel (GK). Simulations were set to represent soybean field trials at F2:4 generation. Heritability was sampled from a uniform distribution U(0,1). The simulated residual-to-spatial ratio between residual variance and spatial variance (Ve:Vs) ranged from 9:1 to 1:9. Experimental settings were conducted under an augmented block design with the systematic distribution of checks accounting for 10% of the plots. Relationship information had a substantial impact on the accuracy of the genetic values (G > A > I) and contributed to the accuracy of spatial effects (30.63–42.27% improvement). Spatial models were ranked based on an improvement to the accuracy of estimative of genetic effects as SD ≥ GK ≥ AR ≥ NG ≥ MA > RC ≥ NS, and to the accuracy of estimative of spatial effects as GK ≥ SD ≥ NG > AR ≥ MA > RC. Estimates of genetic and spatial variance were generally biased downwards, whereas residual variances were biased upwards. The advent of relationship information reduced the bias of all variance components. Spatial methods SD, AR, and GK provided the least biased estimates of spatial and residual variance.

Molecular Profiling of Chilli Germplasm By Using SSR Marker

The molecular characterization of chilli germplasm was done based on estimation of genetic diversity among the germplasm by using SSR markers. Forty chilli germplasms were analyzed using eight SSR primers. The SSR primers produced 30 SSR loci with an average value of 3.75 alleles per SSR locus. The similarity index matrix ranged from zero to 2.74. Polymorphic information content (PIC) of the SSR primers ranged from 0.543 to 0.735 with an average value of 0.658. The highest number (five) of allele was observed in primer CAMS-647, whereas the primers CAMS-864, CAMS-880 and CAMS-885 showed lowest number (three) of allele. The smallest allele was found in case of primer CAMS- 236 (176 bp), while the longest allele was detected for the primer CAMS- 864 (288 bp). Based on similarity matrix using the un-weighed Pair Group Method of Arithmetic Means (UPGMA) dendrogram, chilli germplasms were grouped into four main clusters. SSR markers showed genetic variability in the studied chilli germplasm. SAARC J. Agric., 19(1): 1-13 (2021)

Evaluating the role of reference-genome phylogenetic distance on evolutionary inference.

When a high-quality genome assembly of a target species is unavailable, an option to avoid the costly de novo assembly process is a mapping-based assembly. However, mapping shotgun data to a distant relative may lead to biased or erroneous evolutionary inference. Here, we used short-read data from a mammal (beluga whale) and a bird species (rowi kiwi) to evaluate whether reference genome phylogenetic distance can impact downstream demographic (Pairwise Sequentially Markovian Coalescent) and genetic diversity (heterozygosity, runs of homozygosity) analyses. We mapped to assemblies of species of varying phylogenetic distance (from conspecific to genome-wide divergence of >7%), and de novo assemblies created using cross-species scaffolding. We show that while reference genome phylogenetic distance has an impact on demographic analyses, it is not pronounced until using a reference genome with >3% divergence from the target species. When mapping to cross-species scaffolded assemblies, we are unable to replicate the original beluga demographic results, but are able with the rowi kiwi, presumably reflecting the more fragmented nature of the beluga assemblies. We find that increased phylogenetic distance has a pronounced impact on genetic diversity estimates; heterozygosity estimates deviate incrementally with increasing phylogenetic distance. Moreover, runs of homozygosity are largely undetectable when mapping to any nonconspecific assembly. However, these biases can be reduced when mapping to a cross-species scaffolded assembly. Taken together, our results show that caution should be exercised when selecting reference genomes. Cross-species scaffolding may offer a way to avoid a costly, traditional de novo assembly, while still producing robust, evolutionary inference.

Genomic-polygenic evaluations using random regression models with Legendre polynomials and linear splines for milk yield and fat percentage in the Thai multibreed dairy cattle population

This research aimed to compare single-step random regression models with third-order Legendre polynomials (RRLP) and splines with four (RRSP4) and five (RRSP5) knots for milk yield (MY) and fat percentage (FP) genomic-polygenic evaluations in the Thai multibreed dairy population. Models were compared using estimates of variance components, genetic parameters, goodness of fit, genomic-polygenic EBV (GPEBV) accuracies, and animal rankings. The dataset included pedigree and monthly test-day records (69,029 for MY; 29,878 for FP) of 7,206 first-lactation cows from 761 farms, and genotypic records (74,144 actual and imputed SNP) from 2,661 animals. Variance components and genetic parameters for MY and FP were estimated using REML procedures. Models contained contemporary group (herd-year-season), calving age, heterozygosity, and population lactation curve regression coefficients as fixed effects. Random effects were animal additive genetic, permanent environment random regression coefficients, and residual. The population lactation curve, additive genetic, and permanent environment effects were fitted using regression coefficients of third-order Legendre polynomials for RRLP, splines of four knots for RRSP4, and five knots for RRSP5. The estimates of 305-day additive genetic variances (σ^a2) and heritabilities (h^2) were higher for RRLP (MY: σ^a2 = 279,893.2 kg2, h^2 = 0.27; FP: σ^a2 = 0.10%2, h^2 = 0.16) than for RRSP4 (MY: σ^a2 = 260,178.1 kg2, h^2 = 0.19; FP: σ^a2 = 0.08%2, h^2 = 0.11), and for RRSP5 (MY: σ^a2 = 266,198.0 kg2, h^2 = 0.20; FP: σ^a2 = 0.08%2, h^2 = 0.12). Similarly, RRLP yielded better goodness of fit and higher GPEBV accuracies than RRSP4 and RRSP5. The goodness of fit values for RRLP were 293,813 for -2 log-likelihood (-2logL), 293,855 for the Akaike's information criterion (AIC), and 293,915 for the Bayesian information criterion (BIC). The corresponding values for RRSP4 were 362,738 for -2logL, 362,888 for AIC, and 363,101 for BIC, and those for RRSP5 were 354,473 for -2logL, 354,699 for AIC, and 355,020 for BIC. Lastly, the GPEBV accuracies for RRLP were 49.3% for MY and 38.6% for FP, those for RRSP4 were 47.2% for MY and 37.8% for FP, and the ones for RRSP5 were 47.4% for MY, 37.5% for FP. Rank correlations between animal GPEBV from these three models were high ranging from 0.91 to 0.98 for MY and 0.88 to 0.98 for FY. Results indicated that GPEBV from RRLP should be preferred to GPEBV from RRSP4 and RRSP5 to increase selection responses for MY and FP in the Thai multibreed dairy population.

Genomics and human diversity

The sequencing of the human genome (2003) has been followed by a number of technical developments that allow detailed characterization (including complete sequencing) of the DNA of thousands of individuals. This has provided an estimate of human genetic diversity: approx. 3 million base substitutions within our genome that includes 3,000 million bases. Although the divergence between any two individuals is small, it is responsible for much of the phenotypic diversity observed within the human population, and current techniques make it possible to measure genetic distances between any two individuals whose genome has been analysed. When results from a large sample of persons are analysed by sophisticated multi-dimensional representation, fairly distinct clusters appear in the population, indicating groups of individuals that are more similar to each other than to persons from other groups. Although these groups are defined by genome analysis, without any a priori information on their origin, it turns out that they largely correspond to so-called “ethnic” categories that reflect genetic ancestry (e.g. Europeans, Africans and Asians). Every group is internally quite diverse, but analysis of thousands of genetic markers does indeed differentiate between them. “Race”, as commonly defined, is a very imperfect proxy for these ancestry groups, as it suggests high homogeneity within each group, implies assumptions about behaviour and “character” that have no scientific basis, and is heavily tainted by past uses in support of oppression and even genocide. Thus “ancestry” or “ancestry group” is a highly preferable term, especially now that more and more people are of mixed ancestry. These methods have been widely applied to current populations and to “ancient DNA” from historical samples, and have made possible great strides in understanding the history of our species.

La génomique et la diversité humaine

The sequencing of the human genome (2003) has been followed by a number of technical developments that allow detailed characterization (including complete sequencing) of the DNA of thousands of individuals. This has provided an estimate of human genetic diversity: approx. 3 million base substitutions within our genome that includes 3,000 million bases. Although the divergence between any two individuals is small, it is responsible for much of the phenotypic diversity observed within the human population, and current techniques make it possible to measure genetic distances between any two individuals whose genome has been analysed. When results from a large sample of persons are analysed by sophisticated multi-dimensional representation, fairly distinct clusters appear in the population, indicating groups of individuals that are more similar to each other than to persons from other groups. Although these groups are defined by genome analysis, without any a priori information on their origin, it turns out that they largely correspond to so-called “ethnic” categories that reflect genetic ancestry (e.g. Europeans, Africans and Asians). Every group is internally quite diverse, but analysis of thousands of genetic markers does indeed differentiate between them. “Race”, as commonly defined, is a very imperfect proxy for these ancestry groups, as it suggests high homogeneity within each group, implies assumptions about behaviour and “character” that have no scientific basis, and is heavily tainted by past uses in support of oppression and even genocide. Thus “ancestry” or “ancestry group” is a highly preferable term, especially now that more and more people are of mixed ancestry. These methods have been widely applied to current populations and to “ancient DNA” from historical samples, and have made possible great strides in understanding the history of our species.

Mining and characterization of genic-microsatellites associated with sucrose synthesis and their utility in population structure analysis in sugarcane

Sucrose content is a highly valued trait in sugarcane breeding that is regulated by several genes. Tracking of multiple genes involved in the expression of a complex trait in a highly heterozygous crop like sugarcane during the breeding programme is a huge challenge. Trait tagging through expressed sequence tags (ESTs) derived molecular markers has been considered as powerful tool because they are encoded by functionally relevant elements of the genome and directly linked with the target traits. Functional markers related to sucrose content are very limited in sugarcane, which hindered the genetic improvement of this trait. Seventy-three sucrose rich and fifteen low sucrose clones chosen from twelve full-sib families were used for the study on the development and characterization of genic microsatellites associated with sucrose synthesis. A total of 186 alleles were obtained, with an average of 6.20 alleles per microsatellite marker. Polymorphism Information Content (PIC) values were ranged from 0.31 to 0.83, with a mean value of 0.61. The average values of gene diversity (GD) and resolving power (RP) were 0.60 and 5.14, respectively. The present study identified genic SSR markers for four major enzymes viz., CA076844187 (FK), CA155160295 (SREBF), CA163325191,213 (SREBF), TA38405-4547232 (PPFK), and CA135596178, 225 (DFPP) which could be useful to identify sucrose rich clones in the initial testing stages of the selection programme in sugarcane. Analysis of molecular variance (AMOVA) showed 74 per cent of the total variation within population and 26 per cent among population. The EST-SSR primers showed a high allelic polymorphism, as demonstrated by high genetic diversity estimates (I =1.17, Ho = 0.78). The low mean values of fixation index (FST = 0.078) and the high estimates of gene flow (Nm = 4.166) indicates the low level of population differentiation and maximum genetic exchange between two populations. Based on SSR marker data, eighty-eight clonal accessions were classified broadly into high and low sugar population which corresponded to the molecular diversity of the sucrose specific alleles through structure analysis and principal coordinate analysis. Key wordsSugarcane, sucrose content, genic-microsatellites, AMOVA, population structure

Macroecological trend of increasing values of intraspecific genetic diversity and population structure from temperate to tropical streams

AbstractAimAssuming genetic variants are selectively neutral, estimates of intraspecific genetic diversity and population structure should increase simultaneously in parallel to coalescent time, population size and gene flow. However, other processes, such as genetic drift associated with demographic fluctuations, might cause a loss of genetic diversity while not affecting population structure. In this study, we assess large‐scale patterns of estimates of intraspecific genetic variation across species to determine the roles of dispersal, biogeography, divergence time and demographic fluctuations in decoupling genetic diversity and population structure.LocationPristine first‐order streams distributed in seven regions from Neotropical to boreal climate, covering a gradient of habitat persistence through major biogeographical changes (e.g., Pleistocene glaciations).Time period2008–2010.Major taxa studiedFreshwater insect lineages that differ in dispersal propensity.MethodsIntraspecific nucleotide diversity (π) and population structure (ΦST) were estimated for 33 species using 2,128 sequences of the cox1 gene. The correlation between π and ΦST was tested using linear regression models. The geographical distribution of haplotypes was represented in networks. Phylogenetic trees were time calibrated to determine divergence time.ResultsAt a global scale, a positive relationship between π and ΦST was found. Neotropical species showed the highest values of π and ΦST, probably owing to historical environmental stability. Across Europe, the low estimates of π and the wide array of ΦST values and haplotype networks found across species, lineages and latitude were contrary to the biogeographical and dispersal paradigms.Main conclusionsBeyond the macroecological trend found, genetic trajectories of co‐distributed temperate species were disassociated from their functional traits and probably caused by persistent demographic fluctuations associated with local‐scale habitat instability. Overall, the idiosyncratic relationship between π and ΦST across species prevents the establishment of conclusive global patterns and questions the phylogeographical patterns established when studying a reduced number of co‐distributed species.

Genomics and human diversity

The sequencing of the human genome (2003) has been followed by a number of technical developments that allow detailed characterization (including complete sequencing) of the DNA of thousands of individuals. This has provided an estimate of human genetic diversity: approx. 3 million base substitutions within our genome that includes 3,000 million bases. Although the divergence between any two individuals is small, it is responsible for much of the phenotypic diversity observed within the human population, and current techniques make it possible to measure genetic distances between any two individuals whose genome has been analysed. When results from a large sample of persons are analysed by sophisticated multi-dimensional representation, fairly distinct clusters appear in the population, indicating groups of individuals that are more similar to each other than to persons from other groups. Although these groups are defined by genome analysis, without any a priori information on their origin, it turns out that they largely correspond to so-called “ethnic” categories that reflect genetic ancestry (e.g. Europeans, Africans and Asians). Every group is internally quite diverse, but analysis of thousands of genetic markers does indeed differentiate between them. “Race”, as commonly defined, is a very imperfect proxy for these ancestry groups, as it suggests high homogeneity within each group, implies assumptions about behaviour and “character” that have no scientific basis, and is heavily tainted by past uses in support of oppression and even genocide. Thus “ancestry” or “ancestry group” is a highly preferable term, especially now that more and more people are of mixed ancestry. These methods have been widely applied to current populations and to “ancient DNA” from historical samples, and have made possible great strides in understanding the history of our species.

La génomique et la diversité humaine

The sequencing of the human genome (2003) has been followed by a number of technical developments that allow detailed characterization (including complete sequencing) of the DNA of thousands of individuals. This has provided an estimate of human genetic diversity: approx. 3 million base substitutions within our genome that includes 3,000 million bases. Although the divergence between any two individuals is small, it is responsible for much of the phenotypic diversity observed within the human population, and current techniques make it possible to measure genetic distances between any two individuals whose genome has been analysed. When results from a large sample of persons are analysed by sophisticated multi-dimensional representation, fairly distinct clusters appear in the population, indicating groups of individuals that are more similar to each other than to persons from other groups. Although these groups are defined by genome analysis, without any a priori information on their origin, it turns out that they largely correspond to so-called “ethnic” categories that reflect genetic ancestry (e.g. Europeans, Africans and Asians). Every group is internally quite diverse, but analysis of thousands of genetic markers does indeed differentiate between them. “Race”, as commonly defined, is a very imperfect proxy for these ancestry groups, as it suggests high homogeneity within each group, implies assumptions about behaviour and “character” that have no scientific basis, and is heavily tainted by past uses in support of oppression and even genocide. Thus “ancestry” or “ancestry group” is a highly preferable term, especially now that more and more people are of mixed ancestry. These methods have been widely applied to current populations and to “ancient DNA” from historical samples, and have made possible great strides in understanding the history of our species.