Estimating genetic diversity across the neutral genome with the use of dense marker maps

Background

With the advent of high throughput DNA typing, dense marker maps have become available to investigate genetic diversity on specific regions of the genome. The aim of this paper was to compare two marker based estimates of the genetic diversity in specific genomic regions lying in between markers: IBD-based genetic diversity and heterozygosity.

Methods

A computer simulated population was set up with individuals containing a single 1-Morgan chromosome and 1665 SNP markers and from this one, an additional population was produced with a lower marker density i.e. 166 SNP markers. For each marker interval based on adjacent markers, the genetic diversity was estimated either by IBD probabilities or heterozygosity. Estimates were compared to each other and to the true genetic diversity. The latter was calculated for a marker in the middle of each marker interval that was not used to estimate genetic diversity.

Results

The simulated population had an average minor allele frequency of 0.28 and an LD (r²) of 0.26, comparable to those of real livestock populations. Genetic diversities estimated by IBD probabilities and by heterozygosity were positively correlated, and correlations with the true genetic diversity were quite similar for the simulated population with a high marker density, both for specific regions (r = 0.19-0.20) and large regions (r = 0.61-0.64) over the genome. For the population with a lower marker density, the correlation with the true genetic diversity turned out to be higher for the IBD-based genetic diversity.

Conclusions

Genetic diversities of ungenotyped regions of the genome (i.e. between markers) estimated by IBD-based methods and heterozygosity give similar results for the simulated population with a high marker density. However, for a population with a lower marker density, the IBD-based method gives a better prediction, since variation and recombination between markers are missed with heterozygosity.     

Down-regulation of hypusine biosynthesis in plasmodium by inhibition of S-adenosyl-methionine-decarboxylase

An important issue facing global health today is the need for new, effective and affordable drugs against malaria, particularly in resource-poor countries. Moreover, the currently available antimalarials are limited by factors ranging from parasite resistance to safety, compliance, cost and the current lack of innovations in medicinal chemistry. Depletion of polyamines in the intraerythrocytic phase of P. falciparum is a promising strategy for the development of new antimalarials since intracellular levels of putrescine, spermidine and spermine are increased during cell proliferation. S-adenosyl-methionine-decarboxylase (AdoMETDC) is a key enzyme in the biosynthesis of spermidine. The AdoMETDC inhibitor CGP 48664A, known as SAM486A, inhibited the separately expressed plasmodial AdoMETDC domain with a Km _i of 3 μM resulting in depletion of spermidine. Spermidine is an important precursor in the biosynthesis of hypusine. This prompted us to investigate a downstream effect on hypusine biosynthesis after inhibition of AdoMETDC. Extracts from P. falciparum in vitro cultures that were treated with 10 μM SAM 486A showed suppression of eukaryotic initiation factor 5A (eIF-5A) in comparison to the untreated control in two-dimensional gel electrophoresis. Depletion of eIF-5A was also observed in Western blot analysis with crude protein extracts from the parasite after treatment with 10 μM SAM486A. A determination of the intracellular polyamine levels revealed an approximately 27% reduction of spemidine and a 75% decrease of spermine while putrescine levels increased to 36%. These data suggest that inhibition of AdoMetDc provides a novel strategy for eIF-5A suppression and the design of new antimalarials.     

LINE-1 ORF1 protein enhances Alu SINE retrotransposition

Retroelements have contributed over one third of the human genome mass. The currently active LINE-1 (L1) codes for two proteins (ORF1p and ORF2p), both strictly required for retrotransposition. In contrast, the non-coding parasitic SINE (Alu) only appears to need the L1 ORF2p for its own amplification. This requirement was previously determined using a tissue culture assay system in human cells (HeLa). Because HeLa are likely to express functional L1 proteins, it is possible that low levels of endogenous ORF1p are necessary for the observed tagged Alu mobilization. By individually expressing ORF1 and ORF2 proteins from both human (L1RP and LRE3) and rodent (L1A102 and L1spa) L1 sources, we demonstrate that increasing amounts of ORF1 expressing vector enhances tagged Alu mobilization in HeLa cells. In addition, using chicken fibroblast cells as an alternate cell culture source, we confirmed that ORF1p is not strictly required for Alu mobilization in our assay. Supporting our observations in HeLa cells, we find that tagged Alu retrotransposition is improved by supplementation of ORF1p in the cultured chicken cells. We postulate that L1 ORF1p plays either a direct or indirect role in enhancing the interaction between the Alu RNA and the required factors needed for its retrotransposition.     

Reducing assembly complexity of microbial genomes with single-molecule sequencing

Background

The short reads output by first- and second-generation DNA sequencing instruments cannot completely reconstruct microbial chromosomes. Therefore, most genomes have been left unfinished due to the significant resources required to manually close gaps in draft assemblies. Third-generation, single-molecule sequencing addresses this problem by greatly increasing sequencing read length, which simplifies the assembly problem.

Results

To measure the benefit of single-molecule sequencing on microbial genome assembly, we sequenced and assembled the genomes of six bacteria and analyzed the repeat complexity of 2,267 complete bacteria and archaea. Our results indicate that the majority of known bacterial and archaeal genomes can be assembled without gaps, at finished-grade quality, using a single PacBio RS sequencing library. These single-library assemblies are also more accurate than typical short-read assemblies and hybrid assemblies of short and long reads.

Conclusions

Automated assembly of long, single-molecule sequencing data reduces the cost of microbial finishing to $1,000 for most genomes, and future advances in this technology are expected to drive the cost lower. This is expected to increase the number of completed genomes, improve the quality of microbial genome databases, and enable high-fidelity, population-scale studies of pan-genomes and chromosomal organization.     

Genomic prediction based on data from three layer lines: a comparison between linear methods

Background

The prediction accuracy of several linear genomic prediction models, which have previously been used for within-line genomic prediction, was evaluated for multi-line genomic prediction.

Methods

Compared to a conventional BLUP (best linear unbiased prediction) model using pedigree data, we evaluated the following genomic prediction models: genome-enabled BLUP (GBLUP), ridge regression BLUP (RRBLUP), principal component analysis followed by ridge regression (RRPCA), BayesC and Bayesian stochastic search variable selection. Prediction accuracy was measured as the correlation between predicted breeding values and observed phenotypes divided by the square root of the heritability. The data used concerned laying hens with phenotypes for number of eggs in the first production period and known genotypes. The hens were from two closely-related brown layer lines (B1 and B2), and a third distantly-related white layer line (W1). Lines had 1004 to 1023 training animals and 238 to 240 validation animals. Training datasets consisted of animals of either single lines, or a combination of two or all three lines, and had 30 508 to 45 974 segregating single nucleotide polymorphisms.

Results

Genomic prediction models yielded 0.13 to 0.16 higher accuracies than pedigree-based BLUP. When excluding the line itself from the training dataset, genomic predictions were generally inaccurate. Use of multiple lines marginally improved prediction accuracy for B2 but did not affect or slightly decreased prediction accuracy for B1 and W1. Differences between models were generally small except for RRPCA which gave considerably higher accuracies for B2. Correlations between genomic predictions from different methods were higher than 0.96 for W1 and higher than 0.88 for B1 and B2. The greater differences between methods for B1 and B2 were probably due to the lower accuracy of predictions for B1 (~0.45) and B2 (~0.40) compared to W1 (~0.76).

Conclusions

Multi-line genomic prediction did not affect or slightly improved prediction accuracy for closely-related lines. For distantly-related lines, multi-line genomic prediction yielded similar or slightly lower accuracies than single-line genomic prediction. Bayesian variable selection and GBLUP generally gave similar accuracies. Overall, RRPCA yielded the greatest accuracies for two lines, suggesting that using PCA helps to alleviate the “n ≪ p” problem in genomic prediction.

Electronic supplementary material

The online version of this article (doi:10.1186/s12711-014-0057-5) contains supplementary material, which is available to authorized users.