What we’re reading: Dealing with missing sequence data, SNP2GO, and the challenge of replication in bad results

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In the journals

Ferretti, L., E. Raineri, and S. Ramos-Onsins. 2012. Neutrality tests for sequences with missing data. Genetics 191:1397–401. doi: 10.1534/genetics.112.139949.

At present, most packages for population genetics analyses like DNAsp (Librado and Rozas 2009) deal with missing data simply by removing individuals and/or positions affected with incomplete data. This is a good strategy as long as missing data represent a very minor fraction of the alleles, since in this case they do not affect the power of the analysis. However, there could be situations in which a large amount of missing data are unavoidable.

Szkiba, D., M. Kapun, A. von Haeseler, and M. Gallach. 2014. SNP2GO: Functional analysis of genome-wide association studies. Genetics 197:285–289. doi: 10.1534/genetics.113.160341.

If candidate genes are significantly overrepresented, then one typically concludes that the GO term also contains an overrepresentation of candidate SNPs. While this may be true in many instances, it is certainly not always the case.

In the news

“Git can be an invaluable tool for researchers. It does, however, have a bit of a high activation energy.”

“What’s more important, you might ask, a nice doctor or a good one? A nice professor, or one who knows what she’s talking about? Well of course the answer is, why can’t she be both?”

“… the presence of male experimenters caused mice to display 35 percent less pain than when filmed remotely or in the presence of a female experimenter.”

“The level of criticism it would take to prove a wrong study wrong is higher than that almost any existing study can withstand. That is not encouraging for existing studies.”

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About Jeremy Yoder

Jeremy Yoder is a postdoctoral associate in the Department of Plant Biology at the University of Minnesota. He also blogs at Denim and Tweed and Nothing in Biology Makes Sense!, and tweets under the handle @jbyoder.
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