The main study is very well carried out, and the two associations look to me to be well established. However, there are a few little things about the paper that, when combined with some biased reporting in the press, that have been bothering me. Firstly, the main result that has been reported in the news is that the study found an “IQ gene”, but this was only a very small follow-on in the study, and the evidence underlying it is relatively weak (certainly not the “Best evidence yet that a single gene can affect IQ”, as reported by New Scientist). Secondly, the authors use a misleading reporting of statistics to hide the fact that one of their association could easily be cause by an (already well known) association to general body size.

Read the rest of this entry | Read comments

Thank you to Genomes Unzipped for giving us the opportunity to write about our paper published in Nature Methods [1]. Our goal was to develop a method to identify RNA editing sites using matched DNA and RNA sequencing of the same sample. Looking at the problem initially, it seems straightforward enough to generate a list of variants using the RNA sequencing data and then filter out any variants that also appear in the DNA sequencing. In reality, one must pay close attention to the technical details in order to discern true RNA editing sites from false positives. In this post, we will highlight a couple of key strategies we employed to accurately identify editing sites.

Read the rest of this entry | Read comments

The authors kindly provided us with the raw data, and we ran what are called “null simulations” on their dataset to check to see whether their method could generate false positives. This involved randomly swapping around the genotypes of the 23 individuals, and then analysing these randomised datasets using the same statistical method as the paper. These “null datasets” are random, and have no real association between prosociality and OXTR genotype, so if the author’s method was working properly it would almost never find an association in these datasets. The plot below shows the distribution of the “p-value” from the author’s method in the null datasets – if everything was working properly all of the bars would be the same size:

Read the rest of this entry | Read comments

Today, in Nature, three letters (1, 2, 3) were published on the role of de novo coding mutations in the development of autism. I am lead author on one of these manuscripts, working in collaboration with the ARRA Autism Consortium. In this post, I’ll describe the main findings of our work as they relate to autism and how we approached the interpretation of de novo mutations. In essence, de novo point mutation is likely relevant to autism in ~10% of cases, but a single de novo event is not likely to be sufficient to cause autism. Underscoring this is that fewer than half of the cases had an obviously functional point mutation in the exome. However, three genes, SCN2A, KATNAL2 and CHD8 have emerged as likely candidates for contributing to autism pathogenesis.

De novo is Latin for “from the beginning,” and when describing genetic variation or mutation means that the variant has spontaneously arisen and was not inherited from either parent. In autism, de novo copy number variants are among the earliest clearly identified genetic risk factors (see Sanders et al. and Pinto et al. for reviews). Given that these events are novel, natural selection has not acted on them, except for instances where the point mutation is lethal in early life. With next generation sequencing (NGS), we now have the opportunity to identify these events directly.

In this study we explored the impact of de novo mutations on autism by performing targeted sequencing of the protein-coding regions of the genome (known collectively as the exome, and comprising just 1.5% of the genome as a whole) in 175 mother-father-child trios in which the child was diagnosed as autistic. Having sequence from all three members of each family allowed us to find mutations that had arisen spontaneously in a patient’s genome, rather than being inherited from their parents.

We have made a pre-formatted version of our manuscript available here. In this post I just wanted to highlight some of the key lessons emerging from our study.

Read the rest of this entry | Read comments

A lot of researchers have had a pretty negative reaction to this paper (see Erika’s storify of the twitter coverage). There are lots of legitimate criticism (see Erika’s post for details), but to be honest I suspect that a lot of this is a mixture of indignation and sour grapes that this paper, a not particularly original or particularly well done attempt to answer a question that many other people have answered before, got so much press (including a feature in the NYT). A very large number of people have tried to quantify the potential predictive power of genetics for a number of years – why was there no news feature for me and Jeff, or David Clayton, or Naomi Wray and Peter Visccher, or any of the other large number of stat-gen folks who have been doing exactly these studies for years. ANGER RISING and so forth.

But of course, the reason is relatively obvious.
Read the rest of this entry | Read comments

In this post, I’m going to describe how we came to the conclusion that nearly all of the RDD sites are technical artifacts. For a full discussion, please read the comments themselves.

Position biases in alignments around RDD sites. For each RDD site with at least five reads mismatching the genome, we calculated the fraction of reads with the mismatch (or the match) at each position in the alignment of the RNA-seq read to the genome (on the + DNA strand). Plotted is the average of this fraction across all sites, separately for the alignments which match and mismatch the genome.

Read the rest of this entry | Read comments

Background

Imagine you have genome-wide genetic data (from SNP arrays, genome sequencing, or whatever) from a number of populations in a species. A common way to visualize the relationship between your populations is to use a tree. For example, below I’ve built a tree of the 53 human populations from the Human Genome Diversity Panel (using the data from Li et al. [2]).

Maximum likelihood tree of 53 human populations built using TreeMix.

Oxford Nanopore, who we have written about before, today announced two new sequencing machines to come out this year. The announcement has caused quite a buzz amoungst, well, everyone. Nature, New Scientist, GenomeWeb, BioIT World and Forbes all have reported on it, and bloggers Nick Loman and Keith Robison have also had a chance to talk to some of the Oxford Nanopore peeps about their new toys.

A lot of the interest has come from the (very cool) MinION, a tiny, disposable USB-key sequencer (shown in the picture above) that can sequence about a billion base pairs of DNA, and cost around $500-$900 each. The applications of this are endless – the ability to pick up a bit of biological matter, mix it with a few chemicals, and read whatever DNA is in it, could help with diagnostics, epidemiology, ecology, forensics. It is also (though not quite) the price where hobbyists could consider having a play; perhaps in a few years plug-and-play DIY genetics could be a possibility.

Less immediately striking, but still just as interesting, is the GridION sequencing machine. This is the work-horse of the nanopore sequencing world, made for reading lots of DNA, and scaling up to massive sequencing centers. Obviously, many scientists are going to be very interested in many of the features (notably, the ability to read very long pieces of DNA, a trick that has previously been more-or-less impossible to do reliably). However, what will this announcement mean for those of us who are interested in personal genomics?

Read the rest of this entry | Read comments