A short introduction to Isonymy (surname genetics)

Think you need a lab to do population genetics research?  Chances are, you already have everything you need for basic population structure analysis right at home. All you really need is a phonebook, a set of equation and either a pencil, paper and a calculator or, preferably, a computer with a good spreadsheet program.  What follows here is a very short introduction to using surnames as genetic markers in population and kinship studies.

In our culture, along with other European derived societies, surnames are inherited from the father.  As such, they behave as a system analogous to Y-chromosome inheritance, which has been shown to reflect actual genetic within and between populations.  Surnames have been used for decades by biological anthropologists as measures of genetic relationships.  Medical geneticists have also used surnames to study genetic linkages with various diseases, such as certain cancers. 

There are two basic types of isonymy (literally "same name") studies; random and non-random.  Non-random studies are generally geared toward analyses of inbreeding levels in a population, and are usually centered on levels of surname repetition among married couples.  This was the technique employed by George Darwin (son of Charles Darwin) in his landmark study of inbreeding in first-cousin marriages in England.   

Random isonymy,  by contrast, involves calculating the kinship coefficient for a population based on the repetition of surnames throughout that population.  Calculating the random isonymy of either a single population or between two populations is the first step in any kinship study.  Unfortunately, there are many different methods of computing this figure, since many different researchers have worked out their own methods of calculating random isonymy.  One common method for calculating isonymy within a single population is:

Iii = ∑ n_ik (n_ik – 1)

Ni ( Ni-1)

            Iii equals random isonymy.  n_ik equals the number of occurrences of a particular surname within a population (say, for example, Anderson).  This is multiplied with the same number of the occurrences of Anderson minus 1.  So let’s say that Anderson occurs 6 times in a population.  You would multiply 6 * 5, getting 30.  The next step is to repeat this for every surname in the population.  Surnames that only occur once drop out of the equation (since 1 * 0 is always 0).  Then take the results of all these calculations and add them up, which is indicated by the sigma (∑) symbol.  Moving to the bottom of the equation, Ni equals the total number of surnames in the population.  This is multiplied by the total number of surnames minus 1.  Then divide the numerator by the denominator and you have the random isonymy for that population.

From the random isonymy score, you can calculate other metrics of kinship within a population.  The simplest of these are Laskers coefficient of relationship (which corresponds to Wrights coefficient of relationship) which is obtained by dividing random isonymy by 2 (Lasker, 1977).  Divide random isonymy by 4 and you have the coefficient of inbreeding.  These are the very basic elements of surname genetics, which I hope to explore in greater depth in the course of this blog.

References

Darwin, G.  1875.  Marriage between first cousins and their effects.  The Journal of the Statistical Society of London, 38: 153-184.

Lasker, GW. 1977.  A coefficient of relationship by isonymy: a method for estimating the genetic relationship between populations.  Human Biology, 49: 489-493.

Archaic medical terms

Just a quick post today, but one hopefully valuable to archival researchers.  Anyone interested in historical epidemiology has to deal with the often widely varied terminology for diseases and medical conditions.  For example, ever had a case of Prairie Dig?  How about a bout of Bronze John?  Not to mention the ever-present scourge of Knifegrinders Rot.  Fortunately, there are some handy search references online designed for just this occasion.  One of the best I've found thus far is Rudy's List of Archaic Medical Terms at www.antiquusmorbus.com.  This one is worth just browsing for a look into the state of the art of medicine a century or two ago, such as the notation that "Love is occasionally a cause of disease, especially of insanity (Dunglison, 1855)." 

Here's an instructive quiz question; how many conditions can you find throughout the list somehow related to tuberculosis?


Finally, I'd like to welcome my very first blog follower.  Go to her weblog for a look at her agricultural prowess.

References

Rudy's List of Archaic Medical Terms.  www.antiquusmorbus.com.  Accessed February, 2011.

Dunglison, R, 1855.  Dunglinsons Medical Dictionary: A Dictionary of Medical Science.

Cemeteries in Historical Demography

In their excellent article in the May 2010 issue of the American Journal of Physical Anthropology, Lisa Sattenspiel and Melissa Stoops outline a demographic study utilizing data gathered from cemetery headstones in the city of Columbia, Missouri.  They summarize quite well the potential utility cemetery data have for studies in historical demography, namely that mortality rates gleaned from headstones can fill in the gaps in the historical record.  As the authors note, death registrations were not required for rural areas as late as 1910, leaving some rather sizable gaps in the vital statistics of these areas.  Cemetery data thus provide a supplement to the historical record of mortality statistics, and can be used to tease out significant events in local urban ecology (such as disease outbreaks, seasonality of epidemics, or improvements in sanitation).

This article also points to a sizable gap in historical demographic studies in that the majority of such work is centered on urban populations.  Clearly there is a need to expand the scope of such research into rural communities.  The challenge for cemetery studies within such a rural context is that internments are often far less centralized as in larger population centers.  For example, in many early Euroamerican rural settlements internments often centered on small, family plots now isolated from the general view shed of the modern community.  This would seem to entail a fair amount of field research for the aspiring rural demographer interested in historical mortality rates, although the need for such work is no less apparent.

References

Sattenspiel, L. and Stoops M., 2010.  Gleaning signals about the past from cemetery data.  American Journal of Physical Anthropology, 142:7-22.

Welcome to Southern Tier Biohistory

Welcome to the inaugural post for Southern Tier Biohistory.  My goal for this blog will be to create a nexus for information and research centering on an important subset of anthropological research, namely the use of archival and historical data sources.  My own background as a biological anthropologist will inevitably reflect the emphasis topics relevant to human biology.  However, as this weblog grows I hope to include posting relevant to all four subfields as they relate to the topic of archival data mining.

The genesis for this project lies within my circuitous route to my Ph.D in anthropology.  Prior to my graduate studies, I had been working as a field technician for contract archaeology firms in Upstate New York.  Initially starting graduate school as an archaeology student, I quickly became enamored of biological anthropology and evolutionary theory, which eventually saw me obtaining my Ph.D at Binghamton University with David Sloan Wilson.  However, throughout my graduate studies up to this very day, I continued working as a professional archaeologist, and am currently a project director for the Public Archaeology Facility of Binghamton University.  This then was my dilemma – I needed to unify the academic pursuit I had been engaged in for a significant part of my adult life with the occupation I had been engaged in for an even longer period of my life.  How would I do that?

The answer came in part when I got a hold of the edited volume Human Biologists in the Archives (edited by Ann Herring and Alan Swedlund).  This led to my interest in historical demography and the use of surnames as genetic markers (isonymy), which became a springboard for my current research focus.  Further book hoarding (a fairly common condition among both current and former graduate students, or so I am told) led me to Laskers Surnames and Genetic Structure.  The historical focus of these and other works in biological anthropology forms the nucleus of what I hope to cover in this weblog, and I am eager to hear from other anthropologists working the same vein (especially other bloggers).

Finally, a word about the blog title.  Southern Tier refers to the Southern Tier of New York State.  This is both my home and the focus of the lions share of my current research.  Thus I thought it appropriate to contextualize the work I am doing and create a more “localized” feel to the blog.  Context is essential in any historical (or biohistorical) work, and so the Southern Tier will occasionally figure prominently in the posts ahead.