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.     

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.     

MicroRNA transcriptome profiles during swine skeletal muscle development

Background

Dietary polyunsaturated fatty acids (PUFA), in particular the long chain marine fatty acids docosahexaenoic (DHA) and eicosapentaenoic (EPA), are linked to many health benefits in humans and in animal models. Little is known of the molecular response to DHA and EPA of the small intestine, and the potential contribution of this organ to the beneficial effects of these fatty acids. Here, we assessed gene expression changes induced by DHA and EPA in the wildtype C57BL/6J murine small intestine using whole genome microarrays and functionally characterized the most prominent biological process.

Results

The main biological process affected based on gene expression analysis was lipid metabolism. Fatty acid uptake, peroxisomal and mitochondrial beta-oxidation, and omega-oxidation of fatty acids were all increased. Quantitative real time PCR, and -in a second animal experiment- intestinal fatty acid oxidation measurements confirmed significant gene expression differences and showed in a dose-dependent manner significant changes at biological functional level. Furthermore, no major changes in the expression of lipid metabolism genes were observed in the colon.

Conclusion

We show that marine n-3 fatty acids regulate small intestinal gene expression and increase fatty acid oxidation. Since this organ contributes significantly to whole organism energy use, this effect on the small intestine may well contribute to the beneficial physiological effects of marine PUFAs under conditions that will normally lead to development of obesity, insulin resistance and diabetes.     

Metabolic effects of low cortisol during exercise in humans

This studyexamined the physiological effect of reduced plasma cortisol (C) duringprolonged exercise in humans. The effects of normal C (NC) werecompared with metyrapone-induced low C (LC) on plasma substrateavailability and the respiratory exchange ratio during 2 h of exerciseat ~60% peak O₂ consumption innine subjects. The C responses were compared with preexercise (Pre) levels and with a rest day (Con). At rest, C was attenuated by ~70%for LC compared with NC. At rest, plasma glucose, lactate, glycerol,-hydroxybutyrate, alanine, branched-chain amino acids, insulin,glucagon, growth hormone, epinephrine, and norepinephrine were similarunder LC and NC (P > 0.05). Duringexercise under NC, plasma C increased compared with Pre, whereas itremained unchanged during LC. During NC, plasma C was elevated at 90 min (compared with Con) and at 120 min (compared with Con and Pre). During exercise, plasma glucose decreased to the same extent and lactate was similar under both conditions, whereas plasma glycerol, -hydroxybutyrate, alanine, and branched-chain amino acids were higher (P < 0.01) under NC. Plasmainsulin declined (P = 0.01) to agreater extent under LC, whereas growth hormone, epinephrine, andnorepinephrine tended to be higher (0.05  P  0.10). Plasma glucagon increasedunder both conditions (P < 0.01).The respiratory exchange ratio did not differ between conditions. Weconclude that, during exercise, 1) Caccelerates lipolysis, ketogenesis, and proteolysis;2) under LC, glucoregulatory hormoneadjustments maintain glucose homeostasis; and3) LC does not alter whole body substrate utilization or the ability to complete 2 h of moderate exercise.

     

Ultrastructure of the sodium pump: Comparison of thin sectioning, negative staining, and freeze-fracture of purified, membrane-bound (Na+, K+)-ATPase

Purified (Na+, K+)-ATPase was studied by electron microscopy after thin sectioning, negative staining, and freeze-fracturing, particular emphasis being paid to the dimensions and frequencies of substructures in the membranes. Ultrathin sections show exclusively flat or cup-shaped membrane fragments which are triple-layered along much of their length and have diameters of 0.1-0.6 μm. Negative staining revealed a distinct substructure of particles with diameters between 30 and 50 A and with a frequency of 12,500 +/- 2,400 (SD) per μm(2). Comparisons with sizes of the protein components suggest that each surface particle contains as its major component one large catalytic chain with mol wt close to 100,000 and that two surface particles unite to form the unit of (Na+,K+)-ATPase which binds one molecule of ATP or ouabain. The further observations that the surface particles protrude from the membrane surface and are observed on both membrane surfaces in different patterns and degrees of clustering suggest that protein units span the membrane and are capable of lateral mobility. Freeze-fracturing shows intramembranous particles with diameters of 90-110 A and distributed on both concave and convex fracture faces with a frequency of 3,410 +/- 370 per μm(2) and 390 +/- 170 per μm(2), respectively. The larger diameters and three to fourfold smaller frequency of the intramembranous particles as compared to the surface particles seen after negative staining may reflect technical differences between methods, but it is more likely that the intramembranous particle is an oliogomer composed of two or even more of the protein units which form the surface particles.     

Diluent Composition and the Recovery of Escherichia coli

The ability of 0.1% Trypticase (BBL) to maximize recovery of frozen Escherichia coli on Violet Red Bile Agar is presented.     

Solvent-induced free energy landscape and solute-solvent dynamic coupling in a multielement solute

Molecular dynamics simulations using a simple multielement model solute with internal degrees of freedom and accounting for solvent-induced interactions to all orders in explicit water are reported. The potential energy landscape of the solute is flat in vacuo. However, the sole untruncated solvent-induced interactions between apolar (hydrophobic) and charged elements generate a rich landscape of potential of mean force exhibiting typical features of protein landscapes. Despite the simplicity of our solute, the depth of minima in this landscape is not far in size from free energies that stabilize protein conformations. Dynamical coupling between configurational switching of the system and hydration reconfiguration is also elicited. Switching is seen to occur on a time scale two orders of magnitude longer than that of the reconfiguration time of the solute taken alone, or that of the unperturbed solvent. Qualitatively, these results are unaffected by a different choice of the water-water interaction potential. They show that already at an elementary level, solvent-induced interactions alone, when fully accounted for, can be responsible for configurational and dynamical features essential to protein folding and function.