Typical statistical geneticists spend much of their time working with computers, since much of the statistical analysis of data is now conducted using computer software instead of pencil and paper. Many statistical geneticists are actively engaged in developing new statistical methods for problems that are specific to genetics. Computers are invaluable tools, since much of the initial evaluation of new statistical methods is performed using computer simulations. That is, artificial data are generated using computer algorithms, and the statistical method is evaluated for its ability to identify the specific genetic effects that were simulated. Once a new statistical method is validated using computer simulations it can then be applied to the analysis of real genetic data.

Given the highly interdisciplinary field of statistical genetics, training in multiple different scientific disciplines is necessary. Many statistical geneticists receive four years of undergraduate training in mathematics, statistics, physics, computer science, or some other analytical field of study. This analytical preparation is often necessary for graduate studies in statistical genetics. It is certainly possible, and even desirable in many cases, to receive undergraduate training in biological sciences and then go on to study statistics and genetics in graduate school. There are many different educational paths that a statistical geneticist can take, but the key is interdisciplinary training in an analytical field and genetics. Given the increasing demand

?for statistical geneticists in the job market, there are many graduate programs that now specialize in statistical genetics.

After finishing four to six years of graduate training, one can follow any of several career paths. Many graduates receive additional training through postdoctoral studies. These studies, known as a postdoc, can last from one to four years and involve working with a research team to gain additional experience in a particular area. For example, if a student had focused primarily on training in mathematics and statistics, it would be possible to do a postdoc to receive the necessary additional training in genetics.

Students who want to target a particular disease area such as cancer for their career might seek out a postdoc that provides experience in cancer research. Students who feel adequately prepared for the job market from their graduate training may be able to go right into a faculty position at a university or to a scientist position in industry without postdoctoral training. Statistical geneticists are in high demand, and there are often more job openings than qualified people to fill them. This will certainly be a growth profession for the next decade.

Statistical genetics is a very exciting professional area because it is so new and there is so much demand. It is a rapidly changing field, and there are many fascinating scientific questions that need to be addressed. Additionally, given the interdisciplinary nature of statistical genetics, there are plenty of opportunities to interact with researchers and clinicians in other fields, such epidemiology study of as epidemiology, biochemistry, physiology, pathology, evolutionary biology, incidence and spread °f and anthropology. The salary range for statistical geneticists is quite good, since there is so much demand. This is especially true for university medical schools, where starting annual salaries ranged from $70,000 to $80,000 in 2002. Starting salaries for industry positions such as those in pharmaceutical or biotechnology companies were as much as 25 or 50 percent higher.

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A related discipline is genetic epidemiology. Just as statistical genetics requires a combination of training in statistics and genetics, genetic epidemiology requires training in epidemiology and genetics. Since both disciplines require knowledge of statistical methods, there is significant overlap. A primary difference is that statistical geneticists are often more interested in the development and evaluation of new statistical methods, whereas genetic epidemiologists focus more on the application of statistical methods to biomedical research problems. Statistical genetics is an exciting and rewarding career choice for those who have interest and aptitude in the analytical sciences as well as in genetics and biology. see also Statistics.

Jason H. Moore

Bibliography internet Resource

The Committee of Presidents of Statistical Societies (COPSS) Presents Careers in Statistics.

American Statistical Association. <>.


Statistics is the set of mathematical tools and techniques that are used to analyze data. In genetics, statistical tests are crucial for determining if a particular chromosomal region is likely to contain a disease gene, for instance, or for expressing the certainty with which a treatment can be said to be effective.

Statistics is a relatively new science, with most of the important developments occurring with the last 100 years. Motivation for statistics as a formal scientific discipline came from a need to summarize and draw conclusions from experimental data. For example, Sir Ronald Aylmer Fisher, Karl Pearson, and Sir Francis Galton each made significant contributions to early statistics in response to their need to analyze experimental agricultural and biological data. For example, one of Fisher's interests was whether crop yield could be predicted from meteorological readings. This problem was one of several that motivated Fisher to develop some of the early methods of data analysis. Much of modern statistics can be categorized as exploratory data analysis, point estimation, or hypothesis testing.

The goal of exploratory data analysis is to summarize and visualize data and information in a way that facilitates the identification of trends or interesting patterns that are relevant to the question at hand. A fundamental exploratory data-analysis tool is the histogram, which describes the frequency with which various outcomes occur. Histograms summarize the distribution of the outcomes and facilitate the comparison of outcomes from different experiments. Histograms are usually plotted as bar plots, with the range of outcomes plotted on the x-axis and the frequency of the individual outcome represented by a bar on the y-axis. For instance, one might use a histogram to describe the number of people in a population with each of the different genotypes for the ApoE alleles, which influence the risk of Alzheimer's disease.

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