Poster (download)
86
Ivan A. Kuznetsov1, Sergei A. Slavskii2, Tatiana I. Shashkova3, Georgii A. Bazykin4, Tatiana I. Axenovich5, Fyodor A. Kondrashov6, Yurii S. Aulchenko7
1Center of Life Sciences, Skolkovo Institute of Science and Technology, Moscow, Russia; Laboratory of Theoretical and Applied Functional Genomics, Novosibirsk State University, Novosibirsk, Russia, Ivan.Kuznetsov@skoltech.ru
2Laboratory of Theoretical and Applied Functional Genomics, Novosibirsk State University, Novosibirsk, Russia, slavsky@phystech.edu
3Laboratory of Theoretical and Applied Functional Genomics, Novosibirsk State University, Novosibirsk, Russia, deppt002@gmail.com
4Center of Life Sciences, Skolkovo Institute of Science and Technology, Moscow, Russia, g.bazykin@skoltech.ru
5Laboratory of recombination and segregation analysis, Institute of Cytology and Genetics SB RAS, Novosibirsk, Russia, tatiana.aksenovich@gmail.com
6Institute of Science and Technology, Vienna, Austria, fkondrashov@gmail.com
7Laboratory of recombination and segregation analysis, Institute of Cytology and Genetics SB RAS, Novosibirsk, Russia, yurii@bionet.nsc.ru
The classical approach to analysis of polygenic quantitative traits assumes use of normal approximation and the additivity of effects. For more than a century, adult height served as an exemplary trait justifying this type of analysis and serving as a test case. We demonstrate that the classical approach to analysis of height has met its limits in contemporary large populations. In particular, we demonstrate the existence of weak, but highly significant non-additive interactions of genetic and environmental factors. In the conventional model, the better fit to the data that was achieved by accounting for these interactions came at the expense of the mean-model’s increased complexity. The complexity of the model could be reduced if log-normal approximation was used.
1. Am I right, that log-normal approximation of high is free from interactions with PGHS and PR?
How could explain that?
2. Are you planning to test your hypothesis on data from people of non-European ancestry?
Thank you, Elizaveta.
1. We cannot detect the interaction of the factors on our data for the logarithm of height in contrast with for height. It means that either there no statistical interaction between PGHS and RP or it is much weaker than for non-transformed height. Biologically it could mean that height depends on its factors multiplicatively. Technically, it allows us to simplify the prediction model for human height.
2. In our work, we studied summary data for many, including non-European, populations (Page 2, left panel). To see the described effects on individual data, we need a very huge (hundreds of thousands of samples) datasets. Unfortunately, we don’t expect that such data will be available in the short term.
Thank you for the answer, Ivan.
1. That makes sence, thank you for the interpretation!
2. Oh, sorry, I did not notice that the results were obtained for people from various countries. Now I see. Hope that you will be able to expand your study soon.
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