The Utility of Network Analysis

Like most bioinformatics nerds (or anyone with a facebook account), I’m fascinated by networks. Most people immediately think of protein-protein interaction networks, or biological pathways when thinking about networks, but sometimes representing a problem as a network makes solving problems easier.

Recently, some collaborators from the PAGE study had a list of a few hundred SNPs gathered from multiple loci across the genome. For analysis purposes, they were interested in quantifying the number of loci these SNPs represented – in other words, how many distinct signals were represented by their collection of SNPs.

We had linkage disequilibrium data from the HapMap for all pairs of SNPs, and we filtered this using an r-squared cutoff. What we were left with was a mess of SNP pairs that could be tedious to sort through in a spreadsheet. Instead, I represented each pair of SNPs as an edge in a network and loaded the data into Gephi, which provides some wonderful analysis tools. Suppose my LD data is structured like this:

SNP1	SNP2	d-prime	r-squared
16969968	1051730	0.98	1
2036534	1051730	0.92	0.205
578776	1051730	0.96	0.23
8034191	1051730	1	0.961
8042374	1051730	0.99	0.205
...	...	...	...

In a spreadsheet application, I sorted and filtered the LD pairings I wanted using either the r-squared or the d-prime columns. I then deleted any rows that didn’t meet my cutoff, renamed the header for SNP1 to “Source” and SNP2 to “Target”, and exported the file as a comma-separated file (.csv). I opened Gephi, clicked the “Data Laboratory” tab, and Import Spreadsheet to load my data.



Once loaded, I clicked on the “Overview” tab and I can see my graph. The graph looks like a big mess, but we don’t really care how it looks – we’re going to run an analysis. In the “statistics” tab on the right-hand side, you’ll see an option for “connected components”. This runs an algorithm that picks apart and labels collections of nodes that are connected. Running this only takes a second.



I then click on the “Data Laboratory” tab again, and I can see that my nodes are labeled with an ID. This corresponds to the Locus those SNPs represent.



If you want to actually SEE how these relationships fall out, we’ll need to run a layout engine. Back on the “Overview” tab, on the lower left-hand side, there is a drop-down allowing you to choose a layout engine. I have found YifanHu’s Multilevel to be the quickest and most effective for separating small groups like these. Depending on the size of your graph, it may take a moment to run. Once its finished, you should be able to see the components clearly separated. If you want, you can color code them by clicking the green “refresh” button in the “partition” tab in the upper left corner. This reloads the drop-down menu and will provide you with an option to color the nodes by component ID. Select this, and click apply to see the results!




I’ve used Gephi component analysis to do all kinds of fun things, like the number of families in a study using pairwise IBD estimates, looking at patterns of phenotype sharing in pedigrees, and even visualizing citation networks. Sometimes representing a problem as a graph lets you find patterns more easily than examining tables of numbers.

I'm Starting a New Position at the University of Virginia

I just accepted an offer for a faculty position at the University of Virginia in the Center for Public Health Genomics / Department of Public Health Sciences. Starting in October I will be developing and directing a new centralized bioinformatics core in the UVA School of Medicine. Over the next few weeks I'm taking a much-needed vacation next door in Kauai and then packing up for the move to Charlottesville. Posts here may be sparse over the next few weeks, but once I start my new gig I'll be sure to make up for it. And if you're bioinformatics-savvy and in the job market keep an eye out here - once I figure out what I need I will soon be hiring, and will repost any job announcements here.

I've enjoyed my postdoc here at the University of Hawaii Cancer Center, and there is much I'll miss about island life out here in the Pacific. But I'm very seriously looking forward to getting started in this wonderful opportunity at UVA. Thank you all for your comments, suggestions, and help when I needed it. I'll be back online in a few weeks - until then, follow me on Twitter (@genetics_blog).

Aloha!

True Hypotheses are True, False Hypotheses are False

I just read Gregory Cooper and Jay Shendure's review "Needles in stacks of needles: finding disease-causal variants in a wealth of genomic data" in Nature Reviews Genetics. It's a good review about how to narrow down deleterious disease-causing variants from many, many variants throughout the genome when statistics and genetic information alone isn't enough.

I really liked how they framed the multiple-testing problem that routinely plagues large-scale genetic studies, where nominal significance thresholds can yield many false positives when applied to multiple hypothesis tests:

However, true hypotheses are true, and false hypotheses are false, regardless of how many are tested. As such, the actual 'multiple testing burden' depends on the proportion of true and false hypotheses in any given set: that is, the 'prior probability' that any given hypothesis is true, rather than the number of tests per se. This challenge can thus be viewed as a 'naive hypothesis testing' problem — that is, when in reality only one or a few variants are causal for a given phenotype, but all (or many) variants are a priori equally likely candidates, the prior probability of any given variant being causal is miniscule. As a consequence, extremely convincing data are required to support causality, which is potentially unachievable for some true positives.

Defining the challenge in terms of hypothesis quality rather than quantity, however, points to a solution. Specifically, experimental or computational approaches that provide assessments of variant function can be used to better estimate the prior probability that any given variant is phenotypically important, and these approaches thereby boost discovery power.

Check out the full review at Nature Reviews Genetics:

Needles in stacks of needles: finding disease-causal variants in a wealth of genomic data

Excel Template for Mapping Four 96-Well Plates to One 384-Well Plate

Daniel Cook in Jeff Murray's lab at the University of Iowa put together this handy Excel template for keeping track of how samples from four 96-well plates are interleaved to configure a single 384-well plate using robotic liquid handling systems, like the Hydra II.

Paste in lists of samples on your 96-well plates:

And you'll get out a map of how the 384-well plate layout:

And a summary list:

You can download the Excel file here. Thanks for sharing, Daniel.