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2009

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Chad Dechow, an ASReml user from the Department of Dairy and Animal Science at The Pennsylvania State University, studied herd heritability estimates in over 20,000 herds. He and his colleagues compared covariance parameter estimates in subsets of data, pooled from herds with high, medium or low individual herd heritability estimates. The individual herd heritability estimates, generated from a regression model using ASReml, applied to milk yield, fat yield, protein yield and somatic cell score. These regression techniques stratified the herds by heritability; additive genetic variance increased progressively and permanent environmental variance decreased as herd heritability increased. Future work by the same authors demonstrated that herds with low heritability had a high rate of parent misidentification and that such herds were poor candidates for genetic testing herds.

“We used ASReml,” says Chad, “because it is a very flexible mixed models program that is capable of analyzing large datasets in a very efficient manner.”

ASReml was used to generate individual herd heritabilities; the model and results can be seen in the original paper published in The Journal of Dairy Science 2008. 91:1648-1651

It’s important for breeders to know which factors are genetic and which are affected by environment, for example health events have low heritability indicating that improved management would be important in improving animal health, rather than genetic selection. Understanding which factors can be changed by genetic selection and which through environmental influences is vital as it means that breeders and farmers can plan and manage their herds more effectively; as they understand which aspects can be changed through a breeding programme and which through environmental factors. As heritability differs so significantly between different traits it’s again vital to understand which aspects are most likely to be changed through a breeding program; the higher the heritability a trait is, the more quickly it can be changed through genetic selection; again helping breeders and farmers to plan better, as it can indicate timescales for any genetic changes to take place.

There is no doubt that, as with many things in life, the better understood something is the more effective the planning will be. Research, such as Chad’s is vital to help cattle breeders and farmers breed and manage their herds more effectively. And all good research needs support and suitable tools; ASReml is such a tool. Software that was developed by researchers in animal and crop breeding, it is highly suited to this purpose providing users with the ability to obtain reliable predictions of genetic values. Research needs trustworthy and reliable tools and ASReml is a great fit for the job.

Thanks to Chad Dechow for his help on this piece. The full paper is available here.

Also of interest:

Chad is the editor of The Specht Report - a report on breeding performance.

The links between GenStat and NAG go back many years, but did you know that you can now access many of NAG’s numerical algorithms from within GenStat?

For example, to solve the polynomial in Figure 1 (poly1) for y = 0, set the NAG option name equal to c02agf, the algorithm for solving zeros of polynomials.

Bee Breeding Secrets.

There is something about bees that trigger happiness in people. Whether it’s the thought of honey dripping on toast or admiration for the organisation and industry of the little insects, you don’t have to be a bee keeper to like these pretty and industrious insects. Honey in itself is also highly regarded, be it for the taste, or the health-giving properties that are increasingly being assigned to it. But bees are also important for the pollination in nature and therefore agriculture.

But a bee is not just a bee; different strains and species of bee have different characteristics and behaviour patterns, so bee keepers and breeders need to be able to identify specific strains in order that they breed the most suitable bees for their requirements. There are obvious physical aspects that can be used such as size, colour or shape, but these methods are not foolproof and often hide specific strain differences. Another option is DNA testing, but this is an expensive and time consuming exercise, therefore a method first identified and introduced before the 1960’s can be used, known as morphometry.

Morphometry is a study of bee anatomy to establish race or strain characteristics, that are otherwise difficult to ascertain. The types of anatomy studied are the wings, tongue length, Tomentum width (width of the band of hair on the body segments) and hair length. The most common test and study is on the bee wings, looking at vein formation. Specifically bee breeders look at the “cubital index”, which is the ratio between two vein segments of the cubital cell in a bee’s wing, and the “discoidal shift”, which measures the position of the discoidal joint in relation to the perpendicular through the distal lower joint in the radial cell.

The concept of understanding which bee strain or race is important for bee breeding; as the more purer bee strains can have a better temper (this is not always true, as some inbred bees can be very cross), and therefore there is a less chance of being stung, likewise they seem to keep cleaner and more organised hives than their hybrid cousins. Beyond these “housekeeping” issues there is the bees actual performance, for example researchers have found that certain strains of bee respond quicker to Spring stimulation than others, this is important if the bees are being used for pollination, as it means some strains of bees are more active earlier than others.

The more a bee keeper or bee breeder knows about his bees the more sure he can be about how they will perform and behave. And it seems beewing morphometry can give an indication of the strain or subspecies of Apis mellifera.

The Dark European honey bee, or Apis mellifera mellifera, is defined by having a cubital index of no more than 1.9 and the discoidal shift angle of no more than 0 (although these are ideal standards and not always attainable). Results from the measurements from any hive or colony are displayed on a scattergram and bee keepers and breeders can see how pure their bees are. Jacob Kahn has taken this research further and used GenStat to analyse the measurements more in keeping with quantitative population genetics. He doubled the sample size of 30, normally used by honey bee morphometrists, to a sample size of 60 . 10 of these have had their measurements checked for goodness of fit of normal distribution, and the results showed that only one did not conform to normality. He then looked at any correlation between the cubital index and discoidal shift and found that 6 samples displayed correlation. Jacob hopes that further research and work on this will verify his findings, which would suggest that any correlation indicates genetically purer samples. Jacob suspects that there is a misconception, especially among amateurs, as to what is pure and what is a hybrid. “The term hybrid is generally misunderstood by beekeepers,” says Jacob, “We can talk here about two levels of hybridization: a cross between species can be termed hybridization, but a cross between two strains within a subspecies carrying different alleles of particular loci can also be said to be hybrids. and it is this aspect of population genetics which I am hoping to clarify with the help of GenStat.”

At the moment bee keepers and breeders are content with looking at simple scattergrams to check for bee purity, however Jacob’s work shows that there maybe even more details to discover about bee species or strains. Either way GenStat’s sound statistics has enabled Jacob to dig deeper into the data and initial analysis to find out more about the bees. This additional level of information should allow better definition of variants of Apis m. mellifera which in turn gives bee keepers and breeders greater information on their hives and colonies so they can breed better performing bees.

For more details on GenStat and its capability look at the VSNi website.

Likewise to integrate the polynomial between the values -4 and 3, set the option name to d01ahf, the algorithm for simple integration.

At present many of the algorithms with regards linear programming problems, differentiation, integration and ordinary differential equations have been included. Integral equations and partial differential equations will be included in the 12th edition.

Go to the Help menu, select Examples, then Commands and open the NAG topic to see the full list of routines available as well as examples for each one.

For more information on this, look at Simon Harding’s (VSNi software developer) talk at the Australasian GenStat User Conference.

Latest training courses

For the more advanced GenStat users who are looking for assistance with Analysis of Variance, book onto the Introduction to ANOVA on 9th to 10th July, in Apsley, Hertfordshire, UK. For more details and to book email support or go to the training pages on our website.

The aim of the course is to introduce ANOVA techniques and develop the fundamental knowledge and skills to use them correctly and effectively. The basic principles of experimental design will also be included to aid the effective planning of experiments and investigations.

By the end of the course users will be able to:

Out and about with VSNi

We’re always looking for events we can support and sponsor - so please send us details of any events you are organising or involved in, and as we decide on more events for the future we’ll list them on our website.

• what courses you may wish us to include for 2009 and
• how we deliver our training.

The survey is anonymous, unless you specifically provide your details, and should take no longer than 5 minutes. You will be helping to shape our future direction regarding training and the way we deliver it.

Many thanks in advance.

Click Here to take survey (This will open a new browser window on a site called SurveyMonkey - the service we use for all our surveys.)

Training from VSNi

To visit the Training section of the site, go here. You can also find a link to the survey from this page.

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The Western District of Victoria is known for its heavy clay soils, that are prone to short periods of waterlogging for 2-3 months a year in the Winter; but with the right pasture species livestock producers could increase pasture production during the Summer by utilising this stored soil moisture. Additionally a species that can use out of season rainfall and moisture stored in the soil, would reduce groundwater recharge. One such plant is Summer-active tall fescue.

The experiment, which began in November 2004 with the establishment of the grass, is taking place at the DPI EverGraze research site at Hamilton;  4 grazing system treatments were imposed in a randomised design in September 2006. Since then the plots have been grazed at different levels and herbage mass measured monthly to calculate herbage accumulation.

GenStat’s REML technique was used to model the repeated measurements over time.

This enabled seasonal changes in pasture productivity and quality to be detected, and indicated how the plant responds to climatic events, such as the summer rainfall that is a common characteristic of the research site. Stocking rate, and major husbandry practises, such as lambing and calving, could then be timed to coincide with pasture availability. Within each time period, GenStat was used to determine the effects of grazing treatment on pasture persistence, productivity and quality.  Grazing management was then adjusted, based on the results of the GenStat analysis, to achieve maximum consumption of high quality pasture by livestock, whilst also ensuring the long term survival of the pasture.  Throughout this research, efforts were made to reduce error, and increase the accuracy of the results.  This was achieved by using repeated measurements from fixed quadrants, and also by using GenStat’s graphics capabilities to generate residual plots, which were used to check the normality of the data, and, where needed, impose data transformations.

The study found that the accumulation rate and nutritional value of the Summer-active tall fescue are closely related with its growing conditions. Specific grazing systems and the environmental conditions had an effect on the herbage accumulation and nutritional value. Research, such as Margaret’s is vital in being able to provide sound advice and reliable observations to landowners, and because GenStat was originally developed by statisticians working in agricultural research, it is the most suitable data analysis tool for agricultural research. Couple GenStat’s understanding of the agricultural researchers issues with the reliable and proven statistics, and ethics of good statistical practice and you know your analysis results can be trusted.

Details of Margaret’s project and more information is available at EverGraze. An action sheet based on Margaret’s work on summer active Tall Fescue is here.

Review by John A. Wass, Ph.D.

.

The Getting Started manual describes this software as “…a complete and comprehensive statistics package…” but, actually, there is a lot more to it. Most packages these days have a variety of statistical tests with diagnostics and options, graphics, data handling capacity, a quality (QA) module and an experimental design (DOE) module. This package seems to have been designed with the life scientist in mind (not surprising, as it comes from a spin-off of Rothamsted Research and the company, VSNi, is subtitled ‘software for bioscientists’). As such, it offers several significant extras. For example: meta analysis, data mining algorithms, time series analysis and microarray analysis. I stressed this last feature in the title, as microarray analysis represents a very sophisticated and specialized area of bioinformatics, which is usually only had as highly specialized and expensive commercial software, or freeware with extensive programming requirements.

Read his full article. (Opens a new browser window to Scientific Computing website)

Full list of the GenStat 11th Edition new features.

Download a trial copy or to obtain your upgrade of GenStat.

And yet, thanks to developments in agricultural research, studying all aspects of the problems and issues faced in agricultural production, farmers and agricultural producers are able to plan more effectively and manage their businesses using far more informed decisions than in the past.

In relation to the issues facing potato growers, research conducted by The Nematology and Entomology Research Group at the Crop and Environment Research Centre at the Harper Adams University College in the UK, into the relationship between the potato cyst nematode Globodera rostochiensis (G. rostochiensis) and the diseases caused by Rhizoctonia solani (R. solani) has indicated there are links between the two problems. Both problems can cause severe damage to a potato crop, with deformed or malformed plants that are not marketable as well as producing reduced yields or even potatoes with a lower nutrient value. It’s estimated that potato cyst nematodes could cause annual losses in the region of 300 million Euros to potato producers in the European Community alone; so a better understanding of the problem, which could lead to a way of controlling this threat, could mean huge cost savings and increased production.

The trial, undertaken by Dr. Matthew Back, Placement Manager (Agriculture) / Lecturer in Plant Pathology and the team at Harper Adams University College in 2000 and 2001, was the first to look at the issues under field conditions, rather than in a controlled environment. By looking at the nematodes and the fungus in natural conditions, researchers are able to determine far better how significant the interaction is between the two problems. Two sites were used, and planted with the cultivar Desiree and the plants were monitored for diseases related to the R. solani.

The results were analysed in GenStat. Regression analysis (again in GenStat) showed there were relationships between the density of the nematodes and the incidence and severity of disease caused by R. solani. Likewise simple linear regression analysis showed a significant linear relationship between the tuber yield and the infection of runners with R. solani. Multiple regression analysis showed that interactions between the nematode root invasion and the runner infection had a significant effect on the final tuber yield. The study has shown that there are clear interactions between this nematode and the fungus. There were positive relationships between the nematode densities and infection on all potato parts, but specifically between the invasion of potato roots by juvenile nematodes and the infection of runners with R. solani. It seems that the interaction is indirect given that each problem affects different parts of the potato. Interestingly it seems that the nematode plays an important role in the development of R.solani diseases, but these infections may reduce nematode development.

Studies such as these are vital in planning for future agricultural development. By understanding the relationship between different pests and pathogens, more precise and hopefully more productive growing strategies can be developed.

Full paper published in: European Journal of Plant Pathology, Issue Volume 114, 2 / February, 2006, pages 215-223.

But a bee is not just a bee; different strains and species of bee have different characteristics and behaviour patterns, so bee keepers and breeders need to be able to identify specific strains in order that they breed the most suitable bees for their requirements. There are obvious physical aspects that can be used such as size, colour or shape, but these methods are not foolproof and often hide specific strain differences. Another option is DNA testing, but this is an expensive and time consuming exercise, therefore a method first identified and introduced before the 1960’s can be used, known as morphometry.

Morphometry is a study of bee anatomy to establish race or strain characteristics that are otherwise difficult to ascertain. The types of anatomy studied are the wings, tongue length, Tomentum width (width of the band of hair on the body segments) and hair length. The most common test and study is on the bee wings, looking at vein formation. Specifically bee breeders look at the “cubital index”, which is the ratio between two vein segments of the cubital cell in a bee’s wing, and the “discoidal shift”, which measures the position of the discoidal joint in relation to the perpendicular through the distal lower joint in the radial cell.

The concept of understanding which bee strain or race is important for bee breeding; as the more purer bee strains can have a better temper (this is not always true, as some inbred bees can be very cross), and therefore there is a less chance of being stung, likewise they seem to keep cleaner and more organised hives than their hybrid cousins. Beyond these “housekeeping” issues there is the bees actual performance, for example researchers have found that certain strains of bee respond quicker to Spring stimulation than others, this is important if the bees are being used for pollination, as it means some strains of bees are more active earlier than others.

The more a bee keeper or bee breeder knows about his bees the more sure he can be about how they will perform and behave. And it seems beewing morphometry can give an indication of the strain or subspecies of Apis mellifera.

The Dark European honey bee, or Apis mellifera mellifera, is defined by having a cubital index of no more than 1.9 and the discoidal shift angle of no more than 0 (although these are ideal standards and not always attainable). Results from the measurements from any hive or colony are displayed on a scattergram and bee keepers and breeders can see how pure their bees are. Jacob Kahn has taken this research further and used GenStat to analyse the measurements more in keeping with quantitative population genetics. He doubled the sample size of 30, normally used by honey bee morphometrists, to a sample size of 60 . 10 of these have had their measurements checked for goodness of fit of normal distribution, and the results showed that only one did not conform to normality. He then looked at any correlation between the cubital index and discoidal shift and found that 6 samples displayed correlation. Jacob hopes that further research and work on this will verify his findings, which would suggest that any correlation indicates genetically purer samples. Jacob suspects that there is a misconception, especially among amateurs, as to what is pure and what is a hybrid. “The term hybrid is generally misunderstood by beekeepers,” says Jacob, “We can talk here about two levels of hybridization: a cross between species can be termed hybridization, but a cross between two strains within a subspecies carrying different alleles of particular loci can also be said to be hybrids. and it is this aspect of population genetics which I am hoping to clarify with the help of GenStat.”

At the moment bee keepers and breeders are content with looking at simple scattergrams to check for bee purity, however Jacob’s work shows that there maybe even more details to discover about bee species or strains. Either way GenStat’s sound statistics has enabled Jacob to dig deeper into the data and initial analysis to find out more about the bees. This additional level of information should allow better definition of variants of Apis m. mellifera which in turn gives bee keepers and breeders greater information on their hives and colonies so they can breed better performing bees.

Read more about Jacob and the work of the Galtee Bee Breeding Group.

Darwin was one of the first to propose that the origin of these differences may be explained by sexual selection, such as traits that have evolved through male to male combat. However, the way in which sexual dimorphism actually evolves from a genome that is almost entirely shared between the sexes remains puzzling. That is because male and female traits are controlled by the same set of genes, thus the influence of selection in one sex is expected to be countered by opposing selection in the other sex, a situation termed intralocus sexual conflict.

A classic example of sexual dimorphism, which may have evolved through such male-to-male combat, are the horns of sheep, which are significantly larger in males than in females. The bighorn sheep (Ovis canadensis) is a species found in North America and Siberia, aptly named for their massive, curved horns of males which can weigh up to 15kg (as much as the rest of the bones in the male body). Ewes also have horns but they are much shorter and have less curvature. Prior to the rutting season the rams create a hierarchy for access to the ewes for mating through contests settled by clashing horns. It’s clear that large horns play an important role in determining mating partners for males, however very few studies have been carried out on the considerably smaller horns of the ewes, which seem to have no known fitness benefit. Possibly the horns on ewes exist because of a genetic correlation with male horns, or perhaps they are a defence against predators.

Researchers in Canada, the United States and Scotland chose to look more closely at this phenomenon, and tested for sexual conflict over horn size and body mass in a wild population of bighorn. The study population inhabits Ram Mountain in Alberta, Canada. The 35 year study captured data on rams and ewes including measurements of body mass as well as horn length and circumference at its base to calculate horn volume.

The researchers used an animal model (a form of mixed model incorporating pedigree information, where the phenotype is modelled as a sum of its additive genetic value and other random and fixed effects) and restricted maximum likelihood in ASReml to determine additive genetic (co)variance for male and female traits. ASReml is well suited to this area of work having been developed by statisticians in animal breeding. Its fast and efficient algorithms enable analysis of large and complex data sets such as the one in this study.

The study tested for sexual conflict by estimating quantitative genetic parameters and sex-specific selection coefficients for two sexually dimorphic traits – body mass and horn volume. The traits showed significant additive genetic variance and were positively, but not perfectly, genetically correlated between the sexes. There was also no evidence of sexually antagonistic selection. The intralocus sexual conflict has therefore been resolved, as sex-biased genetic variation has allowed sexual dimorphism to evolve to a level that is currently somewhat satisfactory to both sexes.

The full paper was published in The Proceedings of the Royal Society of Biological Sciences, March 2008, 278, 623-628. Our thanks to the researchers, Dr. David Coltman (Professor) and his PhD student, Jocelyn Poissant for their help on this article. Dr Coltman also recognizes the work of his collaborator Professor Marco Festa-Bianchet.