A simple method to separate base population and segregation effects in genomic relationship matrices

Background

Genomic selection and estimation of genomic breeding values (GBV) are widely used in cattle and plant breeding. Several studies have attempted to detect population subdivision by investigating the structure of the genomic relationship matrix G. However, the question of how these effects influence GBV estimation using genomic best linear unbiased prediction (GBLUP) has received little attention.

Methods

We propose a simple method to decompose G into two independent covariance matrices, one describing the covariance that results from systematic differences in allele frequencies between groups at the pedigree base (G_A^*) and the other describing genomic relationships (G_S) corrected for these differences. Using this decomposition and F_st statistics, we examined whether observed genetic distances between genotyped subgroups within populations resulted from the heterogeneous genetic structure present at the base of the pedigree and/or from breed divergence. Using this decomposition, we tested three models in a forward prediction validation scenario on six traits using Brown Swiss and dual-purpose Fleckvieh cattle data. Model 0 (M0) used both components and is equivalent to the model using the standard G-matrix. Model 1 (M1) used G_S only and model 2 (M2), an extension of M1, included a fixed genetic group effect. Moreover, we analyzed the matrix of contributions of each base group (Q) and estimated the effects and prediction errors of each base group using M0 and M1.

Results

The proposed decomposition of G helped to examine the relative importance of the effects of base groups and segregation in a given population. We found significant differences between the effects of base groups for each breed. In forward prediction, differences between models in terms of validation reliability of estimated direct genomic values were small but predictive power was consistently lowest for M1. The relative advantage of M0 or M2 in prediction depended on breed, trait and genetic composition of the validation group. Our approach presents a general analogy with the use of genetic groups in conventional animal models and provides proof that standard GBLUP using G yields solutions equivalent to M0, where base groups are considered as correlated random effects within the additive genetic variance assigned to the genetic base.     

Dissolved carbon leaching from soil is a crucial component of the net ecosystem carbon balance

Estimates of carbon leaching losses from different land use systems are few and their contribution to the net ecosystem carbon balance is uncertain. We investigated leaching of dissolved organic carbon (DOC), dissolved inorganic carbon (DIC), and dissolved methane (CH₄), at forests, grasslands, and croplands across Europe. Biogenic contributions to DIC were estimated by means of its δ¹³C signature. Leaching of biogenic DIC was 8.3±4.9 g m^?2 yr^?1 for forests, 24.1±7.2 g m^?2 yr^?1 for grasslands, and 14.6±4.8 g m^?2 yr^?1 for croplands. DOC leaching equalled 3.5±1.3 g m^?2 yr^?1 for forests, 5.3±2.0 g m^?2 yr^?1 for grasslands, and 4.1±1.3 g m^?2 yr^?1 for croplands. The average flux of total biogenic carbon across land use systems was 19.4±4.0 g C m^?2 yr^?1. Production of DOC in topsoils was positively related to their C/N ratio and DOC retention in subsoils was inversely related to the ratio of organic carbon to iron plus aluminium (hydr)oxides. Partial pressures of CO₂ in soil air and soil pH determined DIC concentrations and fluxes, but soil solutions were often supersaturated with DIC relative to soil air CO₂. Leaching losses of biogenic carbon (DOC plus biogenic DIC) from grasslands equalled 5–98% (median: 22%) of net ecosystem exchange (NEE) plus carbon inputs with fertilization minus carbon removal with harvest. Carbon leaching increased the net losses from cropland soils by 24–105% (median: 25%). For the majority of forest sites, leaching hardly affected actual net ecosystem carbon balances because of the small solubility of CO₂ in acidic forest soil solutions and large NEE. Leaching of CH₄ proved to be insignificant compared with other fluxes of carbon. Overall, our results show that leaching losses are particularly important for the carbon balance of agricultural systems.     

Reference genes for quantitative reverse transcription-polymerase chain reaction expression studies in wild and cultivated peanut

Background

Molecular genetic studies on rare tumour entities, such as bone tumours, often require the use of decalcified, formalin-fixed, paraffin-embedded tissue (dFFPE) samples. Regardless of which decalcification procedure is used, this introduces a vast breakdown of DNA that precludes the possibility of further molecular genetic testing. We set out to establish a robust protocol that would overcome these intrinsic hurdles for bone tumour research.

Findings

The goal of our study was to establish a protocol, using a modified DNA isolation procedure and quality controls, to select decalcified samples suitable for array-CGH testing. Archival paraffin blocks were obtained from 9 different pathology departments throughout Europe, using different fixation, embedding and decalcification procedures, in order to preclude a bias for certain lab protocols. Isolated DNA samples were subjected to direct chemical labelling and enzymatic labelling systems and were hybridised on a high resolution oligonucleotide chip containing 44,000 reporter elements. Genomic alterations (gains and losses) were readily detected in most of the samples analysed. For example, both homozygous deletions of 0.6 Mb and high level of amplifications of 0.7 Mb were identified.

Conclusions

We established a robust protocol for molecular genetic testing of dFFPE derived DNA, irrespective of fixation, decalcification or sample type used. This approach may greatly facilitate further genetic testing on rare tumour entities where archival decalcified, formalin fixed samples are the only source.     

Serotonin,Eating Behavior,and Fat Intake

There is an intimate relationship between nutritional intake (eating) and serotonin activity. Experimental manipulations (mainly neuropharmacological) of serotonin influence the pattern of eating behavior, subjective feelings of appetite motivation, and the response to nutritional challenges. Similarly, nutritional manipulations (food restriction, dieting, or altered nutrient supply) change the sensitivity of the serotonin network. Traditionally, serotonin has been linked to the macronutrient carbohydrate via the intermediary step of plasma amino acid ratios. However, it has also been demonstrated that 5-HT drugs will reduce energy intake and reverse body weight gain in rats exposed to weight increasing high fat diets. 5-HT drugs can also reduce food intake and block weight gain of rats on a high fat cafeteria diet. Some diet selection studies in rats indicate that the most prominent reduction of macronutrient intake is for fat. These data indicate that 5-HT activity can bring about a reduction in fat consumption. In turn, different types of dietary fat can alter brain 5-HT activity. In human studies the methodology of food choice experiments has often precluded the detection of an effect of 5-HT manipulation on fat intake. However, there is evidence that in obese and lean subjects some 5-HT drugs can readily reduce the intake of high fat foods. Data also suggest that 5-HT activation can lead to a selective avoidance of fat in the diet. These effects of 5-HT on the intake of dietary fat may involve a pre-absorptive mechanism and there is evidence that 5-HT is linked to cholecystokinin and enterostatin. These proposals have theoretical and practical implications and suggest possible strategies to intensify or advance fat-induced satiety signals.