Wednesday, October 06, 2010

MAOA and reconvictions

Helsinki University recommends that the decision to free a murder or keep him in jail should use genetic information among other data. If this is implemented, it is the first time that personality estimates based on genetic tests determine a person's future.

When a man is convicted to life in prison in Finland, he can only be released by pardon. Estimates about his danger to society are used when deciding about pardon. The existing method is to use PCL-R scale to estimate how psychopathic the person is. The steak of the new research is that MAOA gene + PCL-R score together provide even better estimate.

MAOA gene comes in high-activity and low-activity variants. Among convicts with low-activity MAOA, there is no link between PCL-R score are reconviction rate. Among high-activity MAOA convicts, each extra point in PCL-R increases reconviction rate with about 7%.

In many studies MAOA has been linked to depression and psychopathy, but the results are full of "ifs" at best and mutually contradictory at worst. If a person with low-acticity MAOA is exposed to childhood violence, it increases the risk of becoming a psycho. Links between MAOA and depression are contradictory. Individual studies have linked MAOA to economic risk taking and voting: People with high-activity MAOA prefer to take risk and use their vote, while low-activity MAOA carriers prefer to take insurance and vote less often.


Should I start graduate studies next year, I should prepare for it already this year. The first step is to find a research topic. Bioinformatics seems like a good source of research topics. There are new results and new types of data coming out every year, so it should be possible to make solid research by merely applying standard computing science methods to some new problem. In this kind of applied research, the strong programming routine from industry background should be an advantage. This way I could avoid the need to catch up with 30 years of algorithm development history, a burden which handicaps for example state machine or graph theory research. Instead of developing those algorithms, my task would be to pick and combine algorithms and adjust them to the problem at hand. It is not easier, but it is more skill oriented and less memory oriented. Another advantage of applied bioinformatics research is that it has concrete goals to strive at. This does much to avoid buzzword-heavy, bullshitty basic research from which you can see straight away that it is never going to produce anything useful, which makes it extremely demoralizing for people working on it.


J Kujala said...

Bioinformatics doesn't have such a great track record of producing something useful, though they certainly have potential. For information about research topics see:

Simo said...

When doing master's thesis about computer aided language-learning, I did ask those questions and chose research topic accordingly. More about that later.

Should I end up doing bioinformatics, someone else should take responsibility for the relevance of the research topic, since I lack the necessary background.

A colleague of mine did reasearch about genetic fingerprints. The problem was to develop an algorithm to search huge databases of genetic fingerprints. That's the closest contact I've had with bioinformatics. Its undeniable usefulness gives bioinformatics a rosy bias.