Projected benefits from integrating NTD programs in sub-Saharan Africa

The integration of preventive chemotherapy programs (PCPs) targeting multiple neglected tropical diseases (NTDs) with similar strategic approaches offers opportunities for enhanced cost-effectiveness. To estimate the potential cost savings and health outcomes of integrated programs, the data available for five NTDs (lymphatic filariasis, onchocerciasis, intestinal helminthiasis, schistosomiasis and trachoma) can be used to define eligible target populations, the probable overlap of at-risk populations, and the cost per person treated in stand-alone and integrated programs. If all targets for 2006 in sub-Saharan Africa are met, then savings of 26-47% can be projected from such integration (a cost of US dollar 58-81 million versus dollar 110 million for stand-alone PCPs). These first estimates can be refined as empirical data become available from integrated PCPs in the future.     

Nutrigenomics: integrating genomic approaches into nutrition research

It has been suggested that the supermarket of today will be the pharmacy of tomorrow. Such statements have been derived from recognition of our increasing ability to optimize nutrition, and maintain a state of good health through longer periods of life. The new field of nutrigenomics, which focuses on the interaction between bioactive dietary components and the genome, recognizes that current nutritional guidelines may be ideal for only a relatively small proportion of the population. There is good evidence that nutrition has significant influences on the expression of genes, and, likewise, genetic variation can have a significant effect on food intake, metabolic response to food, individual nutrient requirements, food safety, and the efficacy of disease-protective dietary factors. For example, a significant number of human studies in various areas are increasing the evidence for interactions between single nucleotide polymorphisms (SNPs) in various genes and the metabolic response to diet, including the risk of obesity. Many of the same genetic polymorphisms and dietary patterns that influence obesity or cardiovascular disease also affect cancer, since overweight individuals are at increased risk of cancer development. The control of food intake is profoundly affected by polymorphisms either in genes encoding taste receptors or in genes encoding a number of peripheral signaling peptides such as insulin, leptin, ghrelin, cholecystokinin, and corresponding receptors. Total dietary intake, and the satiety value of various foods, will profoundly influence the effects of these genes. Identifying key SNPs that are likely to influence the health of an individual provides an approach to understanding and, ultimately, to optimizing nutrition at the population or individual level. Traditional methods for identification of SNPs may involve consideration of individual variants, using methodologies such as restriction fragment length polymorphisms or quantitative real-time PCR assays. New developments allow identification of up to 500,000 SNPs in an individual, and with increasingly lowered pricings these developments may explode the population-level potential for dietary optimization based on nutrigenomic approaches.     

PRIME: a graphical interface for integrating genomic/proteomic databases

Data mining, finding and integration of information about proteins of interest, is an essential component in modern biological and biomedical research. Even when focusing on a single organism and only on a small number of proteins, there are often dozens fo data sources containing relevant information. We are developing PRIME, a protein information environment, to serve as a virtual central database which integrates distributed heterogeneous information about proteins (linked by common identifier). PRIME has powerful capabilities to visualize all kinds of protein annotation in specialized views. These views can be displayed side by side at the same time and can be synchronized in order to show simultaneously different aspects of identical proteins. These features allow a quick and comprehensive overview of properties of single proteins or protein sets.     

South Africa: from species cradle to genomic applications

The South African government is committed to science and technology innovation, to establishing a knowledge-based economy and to harnessing life-sciences research for health and economic development. Given the constraints and the early stage of development of the field as a whole in South Africa, we found an impressive amount of research on human genomic variation in this country. Encouragingly, South Africa is beginning to apply genomics to address local health needs, including HIV and tuberculosis (TB) infections. We document a number of initiatives in South Africa that are beginning to study genetic variation within the various local indigenous populations. Other early initiatives focus on pharmacogenetic studies, mutation characterization in individual disease genes and genome-wide association studies. Public engagement in genomic issues is spear-headed by The Africa Genome Education Institute.     

Extraction of genomic DNA from yeasts for PCR-based applications

We have developed a quick and low-cost genomic DNA extraction protocol from yeast cells for PCR-based applications. This method does not require any enzymes, hazardous chemicals, or extreme temperatures, and is especially powerful for simultaneous analysis of a large number of samples. DNA can be efficiently extracted from different yeast species (Kluyveromyces lactis, Hansenula polymorpha, Schizosaccharomyces pombe, Candida albicans, Pichia pastoris, and Saccharomyces cerevisiae). The protocol involves lysis of yeast colonies or cells from liquid culture in a lithium acetate (LiOAc)-SDS solution and subsequent precipitation of DNA with ethanol. Approximately 100 nanograms of total genomic DNA can be extracted from 1 × 10(7) cells. DNA extracted by this method is suitable for a variety of PCR-based applications (including colony PCR, real-time qPCR, and DNA sequencing) for amplification of DNA fragments of ≤ 3500 bp.     

Feature selection environment for genomic applications

Background  

Feature selection is a pattern recognition approach to choose important variables according to some criteria in order to distinguish or explain certain phenomena (i.e., for dimensionality reduction). There are many genomic and proteomic applications that rely on feature selection to answer questions such as selecting signature genes which are informative about some biological state, e.g., normal tissues and several types of cancer; or inferring a prediction network among elements such as genes, proteins and external stimuli. In these applications, a recurrent problem is the lack of samples to perform an adequate estimate of the joint probabilities between element states. A myriad of feature selection algorithms and criterion functions have been proposed, although it is difficult to point the best solution for each application.     

Clustering with exclusion zones: genomic applications

Methods for formally evaluating the clustering of events in space or time, notably the scan statistic, have been richly developed and widely applied. In order to utilize the scan statistic and related approaches, it is necessary to know the extent of the spatial or temporal domains wherein the events arise. Implicit in their usage is that these domains have no "holes"-hereafter "exclusion zones"-regions in which events a priori cannot occur. However, in many contexts, this requirement is not met. When the exclusion zones are known, it is straightforward to correct the scan statistic for their occurrence by simply adjusting the extent of the domain. Here, we tackle the more ambitious objective of formally evaluating clustering in the presence of "unknown" exclusion zones. We develop an algorithm for estimating total exclusion zone extent, the quantity needed to correct scan statistic-based inference, using distributional properties of "spacings," and show how bias correction for this estimator can be effected. Performance of the algorithm is assessed via simulation study. We showcase applications to genomic settings for differing marker (event) types-binding sites, housekeeping genes, and microRNAs-wherein exclusion zones can arise through a variety of mechanisms. In several instances, dramatic changes to unadjusted inference that does not accommodate exclusions are evidenced.     

Accurate determination of DNA yield from individual mosquitoes for population genomic applications

Abstract  Accurate estimates of DNA quantity are likely to become increasingly important for successful genomic screening of insect populations via recently developed, highly multiplexed genotyping assays and high-throughput sequencing methods. Here we show that genomic DNA extractions from single Anopheles gambiae Giles using a standard commercial kit-based methodology yield extracts with concentrations below the linear range of spectrophotometric absorbance at 260 nm. Concentrations determined by spectrophotometry were not reproducible, and are therefore neither accurate nor reliable. However, DNA quantification using a fluorescent nucleic acid stain (PicoGreen^®) gave highly reproducible concentration estimates, and indicated that, on average, single mosquitoes yielded approximately 300 ng of DNA. Such a total yield is currently insufficient for many high-throughput genome screening applications, necessitating whole genome amplification of all or most individuals in a population prior to genotyping.     

Snat: a SNP annotation tool for bovine by integrating various sources of genomic information

Background

Genome-wide association (GWA) study has recently become a powerful approach for detecting genetic variants for common diseases without prior knowledge of the variant's location or function. Generally, in GWA studies, the most significant single-nucleotide polymorphisms (SNPs) associated with top-ranked p values are selected in stage one, with follow-up in stage two. The value of selecting SNPs based on statistically significant p values is obvious. However, when minor allele frequencies (MAFs) are relatively low, less-significant p values can still correspond to higher odds ratios (ORs), which might be more useful for prediction of disease status. Therefore, if SNPs are selected using an approach based only on significant p values, some important genetic variants might be missed. We proposed a hybrid approach for selecting candidate SNPs from the discovery stage of GWA study, based on both p values and ORs, and conducted a simulation study to demonstrate the performance of our approach.

Results

The simulation results showed that our hybrid ranking approach was more powerful than the existing ranked p value approach for identifying relatively less-common SNPs. Meanwhile, the type I error probabilities of the hybrid approach is well-controlled at the end of the second stage of the two-stage GWA study.

Conclusions

In GWA studies, SNPs should be considered for inclusion based not only on ranked p values but also on ranked ORs.     

Design of reference populations for genomic selection in crossbreeding programs

Background

In crossbreeding programs, genomic selection offers the opportunity to make efficient use of information on crossbred (CB) individuals in the selection of purebred (PB) candidates. In such programs, reference populations often contain genotyped PB animals, although the breeding objective is usually more focused on CB performance. The question is what would be the benefit of including a larger proportion of CB individuals in the reference population.

Methods

In a deterministic simulation study, we evaluated the benefit of including various proportions of CB animals in a reference population for genomic selection of PB animals in a crossbreeding program. We used a pig breeding scheme with selection for a moderately heritable trait and a size of 6000 for the reference population.

Results

Applying genomic selection to improve the performance of CB individuals, with a genetic correlation between PB and CB performance (r_PC) of 0.7, selection accuracy of PB candidates increased from 0.49 to 0.52 if the reference population consisted of PB individuals, it increased to 0.55 if the reference population consisted of the same number of CB individuals, and to 0.60 if the size of the CB reference population was twice that of the reference population for each PB line. The advantage of using CB rather than PB individuals increased linearly with the proportion of CB individuals in the reference population. This advantage disappeared quickly if r_PC was higher or if the breeding objective put some emphasis on PB performance. The benefit of adding CB individuals to an existing PB reference population was limited for high r_PC.

Conclusions

Using CB rather than PB individuals in a reference population for genomic selection can provide substantial advantages, but only when correlations between PB and CB performances are not high and PB performance is not part of the breeding objective.     

Hybrid wheat: quantitative genetic parameters and consequences for the design of breeding programs

Key message

Commercial heterosis for grain yield is present in hybrid wheat but long-term competiveness of hybrid versus line breeding depends on the development of heterotic groups to improve hybrid prediction.

Abstract

Detailed knowledge of the amount of heterosis and quantitative genetic parameters are of paramount importance to assess the potential of hybrid breeding. Our objectives were to (1) examine the extent of midparent, better-parent and commercial heterosis in a vast population of 1,604 wheat (Triticum aestivum L.) hybrids and their parental elite inbred lines and (2) discuss the consequences of relevant quantitative parameters for the design of hybrid wheat breeding programs. Fifteen male lines were crossed in a factorial mating design with 120 female lines, resulting in 1,604 of the 1,800 potential single-cross hybrid combinations. The hybrids, their parents, and ten commercial wheat varieties were evaluated in multi-location field experiments for grain yield, plant height, heading time and susceptibility to frost, lodging, septoria tritici blotch, yellow rust, leaf rust, and powdery mildew at up to five locations. We observed that hybrids were superior to the mean of their parents for grain yield (10.7 %) and susceptibility to frost (?7.2 %), leaf rust (?8.4 %) and septoria tritici blotch (?9.3 %). Moreover, 69 hybrids significantly (P < 0.05) outyielded the best commercial inbred line variety underlining the potential of hybrid wheat breeding. The estimated quantitative genetic parameters suggest that the establishment of reciprocal recurrent selection programs is pivotal for a successful long-term hybrid wheat breeding.     

Advances in high-resolution quantitative proteomics: implications for clinical applications

The advances in high-resolution mass spectrometry instrumentation, capable of accurate mass measurement and fast acquisition, have enabled new approaches for targeted quantitative proteomics. More specifically, analyses performed on quadrupole-orbitrap mass spectrometers operated in parallel reaction monitoring (PRM) mode leverage the intrinsic high resolving power and trapping capabilities. The PRM technique offers unmatched degrees of selectivity and analytical sensitivity, typically required to analyze peptides in complex samples, such as those encountered in biomedical research or clinical studies. The features of PRM have provoked a paradigm change in targeted experiments, by decoupling acquisition and data processing. It has resulted in a new analytical workflow comprising distinct methods for each step, thus enabling much larger flexibility. The PRM technique was further enhanced by a new data acquisition scheme, allowing dynamic parameter settings. The potential of the technique may radically impact future quantitative proteomics studies.     

An infinitesimal model for quantitative trait genomic value prediction

We developed a marker based infinitesimal model for quantitative trait analysis. In contrast to the classical infinitesimal model, we now have new information about the segregation of every individual locus of the entire genome. Under this new model, we propose that the genetic effect of an individual locus is a function of the genome location (a continuous quantity). The overall genetic value of an individual is the weighted integral of the genetic effect function along the genome. Numerical integration is performed to find the integral, which requires partitioning the entire genome into a finite number of bins. Each bin may contain many markers. The integral is approximated by the weighted sum of all the bin effects. We now turn the problem of marker analysis into bin analysis so that the model dimension has decreased from a virtual infinity to a finite number of bins. This new approach can efficiently handle virtually unlimited number of markers without marker selection. The marker based infinitesimal model requires high linkage disequilibrium of all markers within a bin. For populations with low or no linkage disequilibrium, we develop an adaptive infinitesimal model. Both the original and the adaptive models are tested using simulated data as well as beef cattle data. The simulated data analysis shows that there is always an optimal number of bins at which the predictability of the bin model is much greater than the original marker analysis. Result of the beef cattle data analysis indicates that the bin model can increase the predictability from 10% (multiple marker analysis) to 33% (multiple bin analysis). The marker based infinitesimal model paves a way towards the solution of genetic mapping and genomic selection using the whole genome sequence data.