Pooling mRNA in microarray experiments and its effect on power

MOTIVATION: Microarrays can simultaneously measure the expression levels of many genes and are widely applied to study complex biological problems at the genetic level. To contain costs, instead of obtaining a microarray on each individual, mRNA from several subjects can be first pooled and then measured with a single array. mRNA pooling is also necessary when there is not enough mRNA from each subject. Several studies have investigated the impact of pooling mRNA on inferences about gene expression, but have typically modeled the process of pooling as if it occurred in some transformed scale. This assumption is unrealistic. RESULTS: We propose modeling the gene expression levels in a pool as a weighted average of mRNA expression of all individuals in the pool on the original measurement scale, where the weights correspond to individual sample contributions to the pool. Based on these improved statistical models, we develop the appropriate F statistics to test for differentially expressed genes. We present formulae to calculate the power of various statistical tests under different strategies for pooling mRNA and compare resulting power estimates to those that would be obtained by following the approach proposed by Kendziorski et al. (2003). We find that the Kendziorski estimate tends to exceed true power and that the estimate we propose, while somewhat conservative, is less biased. We argue that it is possible to design a study that includes mRNA pooling at a significantly reduced cost but with little loss of information.     

Response and inbreeding from a genomic selection experiment in layer chickens

Background

Genomic selection (GS) using estimated breeding values (GS-EBV) based on dense marker data is a promising approach for genetic improvement. A simulation study was undertaken to illustrate the opportunities offered by GS for designing breeding programs. It consisted of a selection program for a sex-limited trait in layer chickens, which was developed by deterministic predictions under different scenarios. Later, one of the possible schemes was implemented in a real population of layer chicken.

Methods

In the simulation, the aim was to double the response to selection per year by reducing the generation interval by 50 %, while maintaining the same rate of inbreeding per year. We found that GS with retraining could achieve the set objectives while requiring 75 % fewer reared birds and 82 % fewer phenotyped birds per year. A multi-trait GS scenario was subsequently implemented in a real population of brown egg laying hens. The population was split into two sub-lines, one was submitted to conventional phenotypic selection, and one was selected based on genomic prediction. At the end of the 3-year experiment, the two sub-lines were compared for multiple performance traits that are relevant for commercial egg production.

Results

Birds that were selected based on genomic prediction outperformed those that were submitted to conventional selection for most of the 16 traits that were included in the index used for selection. However, although the two programs were designed to achieve the same rate of inbreeding per year, the realized inbreeding per year assessed from pedigree was higher in the genomic selected line than in the conventionally selected line.

Conclusions

The results demonstrate that GS is a promising alternative to conventional breeding for genetic improvement of layer chickens.     

Persistence of accuracy of genomic estimated breeding values over generations in layer chickens

Background

The predictive ability of genomic estimated breeding values (GEBV) originates both from associations between high-density markers and QTL (Quantitative Trait Loci) and from pedigree information. Thus, GEBV are expected to provide more persistent accuracy over successive generations than breeding values estimated using pedigree-based methods. The objective of this study was to evaluate the accuracy of GEBV in a closed population of layer chickens and to quantify their persistence over five successive generations using marker or pedigree information.

Methods

The training data consisted of 16 traits and 777 genotyped animals from two generations of a brown-egg layer breeding line, 295 of which had individual phenotype records, while others had phenotypes on 2,738 non-genotyped relatives, or similar data accumulated over up to five generations. Validation data included phenotyped and genotyped birds from five subsequent generations (on average 306 birds/generation). Birds were genotyped for 23,356 segregating SNP. Animal models using genomic or pedigree relationship matrices and Bayesian model averaging methods were used for training analyses. Accuracy was evaluated as the correlation between EBV and phenotype in validation divided by the square root of trait heritability.

Results

Pedigree relationships in outbred populations are reduced by 50% at each meiosis, therefore accuracy is expected to decrease by the square root of 0.5 every generation, as observed for pedigree-based EBV (Estimated Breeding Values). In contrast the GEBV accuracy was more persistent, although the drop in accuracy was substantial in the first generation. Traits that were considered to be influenced by fewer QTL and to have a higher heritability maintained a higher GEBV accuracy over generations. In conclusion, GEBV capture information beyond pedigree relationships, but retraining every generation is recommended for genomic selection in closed breeding populations.     

Effects of ad libitum and restricted feeding on early production performance and body composition of Yorkshire pigs selected for reduced residual feed intake

Residual feed intake (RFI), defined as the difference between observed and expected feed intake based on growth and backfat, has been used to investigate genetic variation in feed efficiency in cattle, poultry and pigs. However, little is known about the biological basis of differences in RFI in pigs. To this end, the objective of this study was to evaluate the fifth generation of a line of pigs selected for reduced RFI against a randomly selected Control line for performance, carcass and chemical carcass composition and overall efficiency. Here, emphasis was on the early grower phase. A total of 100 barrows, 50 from each line, were paired by age and weight (22.6 ± 3.9 kg) and randomly assigned to one of four feeding treatments in 11 replicates: ad libitum (Ad), 75% of Ad (Ad75), 55% of Ad (Ad55) and weight stasis (WS), which involved weekly adjustments in intake to keep body weight (BW) constant for each pig. Pigs were individually penned (group housing was used for selection) and were on treatment for 6 weeks. Initial BW did not significantly differ between the lines (P > 0.17). Under Ad feeding, the low RFI pigs consumed 8% less feed compared with Control line pigs (P < 0.06), had less carcass fat (P < 0.05), but with no significant difference in growth rate (P > 0.85). Under restricted feeding, low RFI pigs under the Ad75 treatment had a greater rate of gain while consuming the same amount of feed as Control pigs. Despite the greater gain, no significant line differences in carcass composition or carcass traits were observed. For the WS treatment, low RFI pigs had similar BW (P > 0.37) with no significant difference in feed consumption (P > 0.32). Overall, selection for reduced RFI has decreased feed intake, with limited differences in growth rate but reduced carcass fat, as seen under Ad feeding. Collectively, results indicate that the effects of selection for low RFI are evident during the early grower stage, which allows for greater savings to the producer.     

Airway cellularity, lipid laden macrophages and microbiology of gastric juice and airways in children with reflux oesophagitis

Background and methods

Human metapneumovirus (hMPV) is a recently discovered respiratory virus associated with bronchiolitis, pneumonia, croup and exacerbations of asthma. Since respiratory viruses are frequently detected in patients with acute exacerbations of COPD (AE-COPD) it was our aim to investigate the frequency of hMPV detection in a prospective cohort of hospitalized patients with AE-COPD compared to patients with stable COPD and to smokers without by means of quantitative real-time RT-PCR.

Results

We analysed nasal lavage and induced sputum of 130 patients with AE-COPD, 65 patients with stable COPD and 34 smokers without COPD. HMPV was detected in 3/130 (2.3%) AE-COPD patients with a mean of 6.5 × 10⁵ viral copies/ml in nasal lavage and 1.88 × 10⁵ viral copies/ml in induced sputum. It was not found in patients with stable COPD or smokers without COPD.

Conclusion

HMPV is only found in a very small number of patients with AE-COPD. However it should be considered as a further possible viral trigger of AE-COPD because asymptomatic carriage is unlikely.     

<Emphasis Type="Italic">Arabidopsis</Emphasis> ETO1 specifically interacts with and negatively regulates type 2 1-aminocyclopropane-1-carboxylate synthases

Background  

In Arabidopsis, ETO1 (ETHYLENE-OVERPRODUCER1) is a negative regulator of ethylene evolution by interacting with AtACS5, an isoform of the rate-limiting enzyme, 1-aminocyclopropane-1-carboxylate synthases (ACC synthase or ACS), in ethylene biosynthetic pathway. ETO1 directly inhibits the enzymatic activity of AtACS5. In addition, a specific interaction between ETO1 and AtCUL3, a constituent of a new type of E3 ubiquitin ligase complex, suggests the molecular mechanism in promoting AtACS5 degradation by the proteasome-dependent pathway. Because orthologous sequences to ETO1 are found in many plant species including tomato, we transformed tomato with Arabidopsis ETO1 to evaluate its ability to suppress ethylene production in tomato fruits.     

Evaluation of linkage disequilibrium measures between multi-allelic markers as predictors of linkage disequilibrium between markers and QTL

Effectiveness of marker-assisted selection (MAS) and quantitative trait loci (QTL) mapping using population-wide linkage disequilibrium (LD) between markers and QTL depends on the extent of LD and how it declines with distance in a population. Because marker-QTL LD cannot be observed directly, the objective of this study was to evaluate alternative measures of observable LD between multi-allelic markers as predictors of usable LD of multi-allelic markers with presumed biallelic QTL. Observable LD between marker pairs was evaluated using eight existing measures and one new measure. These consisted of two pooled and standardized measures of LD between pairs of alleles at two markers based on Lewontin's LD measure, two pooled measures of squared correlations between alleles, one standardized measure using Hardy-Weinberg heterozygosities, and four measures based on the chi-square statistic for testing for association between alleles at two loci. In simulated populations with a range of LD generated by drift and a range of marker polymorphism, marker-marker LD measured by a standardized chi-square statistic (denoted chi(2')) was found to be the best predictor of useable marker-QTL LD for a group of multi-allelic markers. Estimates of the level and decline of marker-marker LD with distance obtained from chi(2') were linearly and highly correlated with usable LD of those markers with QTL across population structures and marker polymorphism. Corresponding relationships were poorer for the other marker-marker LD measures. Therefore, when LD is generated by drift, chi(2') is recommended to quantify the amount and extent of usable LD in a population for QTL mapping and MAS based on multi-allelic markers.